{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Week2 - Pima Indians Diabetes Data Set分类练习(Logistic回归)\n",
    "数据集只有一个文件(diabetes.csv):Pima Indians Diabetes Dataset 包括根据医疗记录的比马印第安人5年内糖尿病的发病情况，这是一个两类分类问题。每个类的样本数目数量不均等。一共有768个样本，每个样本有8个输入变量和1个输出变量。缺失值通常用零值编码。\n",
    "\n",
    "Pregnancies: 怀孕次数 <br>\n",
    "Glucose: 口服葡萄糖耐受试验中，2小时的血浆葡萄糖浓度。 <br>\n",
    "BloodPressure: 舒张压(mm Hg) <br>\n",
    "SkinThickness: 三头肌皮肤褶层厚度(mm) <br>\n",
    "Insulin: 2小时血清胰岛素含量(μU/ ml) <br>\n",
    "BMI: 体重指数(体重，kg /(身高，m)^ 2) <br>\n",
    "DiabetesPedigreeFunction: 糖尿病家族史 <br>\n",
    "Age: 年龄(岁) <br>\n",
    "Outcome: 输出变量/类别标签(0 或 1，出现糖尿病为 1, 否则为 0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import必要的模块\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('diabetes.csv')\n",
    "data.head()\n",
    "#data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W8/QlJ0nSHNXnLqbhdSEeB+4AVoykGknSxOgzBuG6EJI0D0235OhHpjmuquqjI6hHkjQh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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110934198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 看各类样本分布\n",
    "sns.countplot(data.Outcome)\n",
    "plt.xlabel('Outcome')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115863978>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EB4YxqvlQBtcfSIPPGmOZP09mm/BeSe1nKxPmMrzWAKa6jWXl0CV0ndKHAp/Y\npK5HZHxeU5UBwkMiaF+9M72bDeT7b1cydcUkcpnnMpBr0rYRR/48mm5bU6s/tR7o3fVH2FlnFFdn\nbabisMRwadi1B+xuPJ79LadQYbAbmuym6epLotgovQCV29XFoWIxTqw1HKPMbZ2HTou/YtuY1Ubf\nj6ndy8b2uF17tOD68SsGztlYUv8OpS5b7tM62FYoxoU1+wDQmGhwqFaKYzN/Z53bFPJ8Yk2FDvWN\nUJrB/SUEdt/0I3DWT2lWkdOpJPLla17fe5yxvn+KjDf+YwRCiOZCiLtCiPtCiPGp5H8ihDguhLim\n73S0zKhO5Vz/wwghVuh7lWk/IhrSGNgupQwDkFK+feytBfyu//tXoK7+7zPAOiFEf0CrT3MFeggh\nrgMXgHxAiTTaN0AIcVkIcfnhMx+DvPCgMPLb5U84zmeXnyfBme99PgmJwPfeY8pUL5vpsu+TiKBw\nrOwSn/Ct7PIRmQl7I0OeABDqG8yd839RuHyxVOVCAkMpYF8g4biAXX7CgsPSlNFqNZhZmBH1JIqY\nNzFEPYkC4O5NL/y9A/ikWOKIg2PZYmhNtNy96ZVuW58HRmBmn9hzy2VnxYvgJ2nKP0olbAzw9H4A\nsS9fk7eUcaMeT4MisLRPPMeWdvmICkmp17FOeRoPbse6fguJe5MYFsxunpM+v4zl4KKtPL523yid\nAOFB4eQzuJfzGX0vl6xSimY9W/L96bV0m9SL+u0b0Xlcd6PKRgdFJIR5AXLbWfEslfNcpE45ag9u\nw/Z+ixPsjQ6MIPhvHyJ9Q5Fx8XgduoJt+SIZ6owNDMM0ia2mtvmITWKrxjwnOUoWptjm2ZQ69SO5\nKpei8A+TyVnBMUEmT+t/OSQMWdpzFUJogRVAC6As0FkIkfwHZzKwVUpZGd1Q3cqM6lXO9b/F30DC\nAJmU8mugCWCdTC4Ww2uXQ/+/wLhnaqmv/0t0N00h4LoQIp++jiFSSif9p6iU8nCqlUi5VkrpLKV0\nLmZe2CDvvqcXdkXtKVDIBhNTE+q41eOS+wUjmqYLKWfLng0AMwszSjuXIeCBfwalPiwPPe9jW9QO\n60IF0JqaUNOtLlfdjXsmymVhhkk23dxC87y5KeFcGn8v31Rl71y/g0PRgtgVssXE1IQmbRtz+rDh\nzNTTh8/SsoMrAA1bNeDKmWsA5LGyRKMfv7b/xI5CRR3wfxyYUK5p2yYc+fNYhu0Nv/6Q3EVtMS9k\njcZUS5G2NfE9bBhwyV00seft0NSJqEe68U7zQtYIra4NZgXzYVHMjme+oRnqBPDzfED+IrbkdbBG\na6qlklstbrlfMZCxL1eEz2YHuW5sAAAgAElEQVT3Y32/hTwPj0pI15pq6bFmJFd2nOLmfuPuw7c8\n8PQyuLa13epy2f2iUWW/H7aEr2v3Z0jdAfw2ax0ndxxn07xfjSob4PmQvEVtsdSf5zJuNfFyNzzP\nNuUK03xOH7b3XcyLJPYGej4kh2Uuclrp5hcUrl2OMK+Mv0MvbniRvYg9pg42CFMTLN3qE3Uk0db4\n6BfcrtqVu/X6cbdeP15cu4tP/5m8vKl/WBECy5Z1PirnClQH7kspH0op3wCbgeSzDiXwdpDeEggg\nA9Rs4f8Wx4DZQohBUspV+rRcqch5A60BhBBVgLdh26PATiHEEilluBDCSt97PYvuaetXoCtwWl+2\nuJTyAnBBCOGGzskeAgYJIY5JKWOEECUBfynl88wYEh8Xz49T1jB5wzTd6wtbj+Dn5UvHkV14cOM+\nl49cpHhFR8aunYiZpTnOTavRcUQXRrgMxsGxED0n90FKiRCC3Wv/5PFdn4yVGsGYqXO5dO0GkZFR\nNGnXja/6duczt2bvXG98XDwbpvzImA1T0Gg1nNx6FH8vX9qP7MSjGw+4duQSRSs6MnztOMwszXBq\nWo32IzoywWU4BUs40Hv2l8h4idAI9q7aaTDLOClxcfEsmfw9i3+fh1ajZe+WAzy6502/0b2443mP\n0+5n2bt5P98sm8iW078SFRnN1K90M4Wdalak3+jexMbFER8Xz4IJS4iOjE6ou7FbA0Z3n5ChrTIu\nnouT19P097G6V3G2nODpPX8qjf6McM9H+LlfpXQvV+zqlSM+No43T59zZvgaAApUL0n5r92Ij41D\nxksuTFzH6yfPjD7Hu6aso9+GCWi0Gi5t9SDYyw/XEZ/jd/MRt45codWELmTLlYNuK3UjIpH+4azr\nv5CKrWpRrHppzPKa4/y5Ljy6ZfRqAm9lfF/Fx8Xz85QfmLhhKhqtFg/9vdxhZGce3rjPlSOXKF7R\nkVFrx2NmaU7Vps50GNGZ0S5DjbIrLWRcPO5T1tNpw1iEVsONrScI8/Kn3sjPCLzxiPtHrtJoYmey\n5crBpyt1uqICwtnebzEyXnJs1ia6/D4BhCDo5iOubzqesdK4eAKmrqbohm91r+JsO8Jrr8cUGNGV\nlze9iD6S/kOFWfVyxASFEeNr3Hj2PyYuS3fFKQgkfZr1A5LP4poGHBZCDAHMgKYZVSoyMw6m+PcR\nQtihexWnBhAKPAdWA8HoX8URQuQEdgEFgEvowrwtpJTeQoiewBggDrgmpeyln+z0M5BfX2dvKeVj\nIcQOdCFfgc4xD9f/PRNw0/8dCrSTUqa7+ePnhdu89xvpf20/14exke9d5//cfq7yw+zn6sT738+1\n9Qfcz7XCoz0Zz65Kh5cbvzH69yZXt5kDgaTvn62VUq59eyCE6IBu7kk//XF3oLqUckgSmZHo/OUi\nIUQt4CegvJRpD+qqnut/DCllILpeZmp46GVeohsbTa38emB9sjRvdOOxyWXbJ09DF/6YqP8oFArF\nf49MvOeqd6Rr0xHxQxe1e4sDKcO+fYHm+vrOCSFyoOushJAGasxVoVAoFB8XWTvmegkoIYQoKoTI\nhq5zszuZzGN0818QQpRBN88l3ckCqueqUCgUio+LLBzOlFLGCiEGo5tvogV+llL+LYSYDlyWUu4G\nRgE/CCFGoIvu9ZIZjKkq56pQKBSKj4ssXhxCSrkf2J8sbUqSv28BdTJTp3KuCoVCofi4UJulKxQK\nhUKRtRi1CcEHRjlXhUKhUHxcqC3nFAqFQqHIYj6CLeeUc1VkCaYf4K2uD7WYw89XsnYvWGMZ6/z+\nXz3+LS7zC85nBc3i82cs9C9wPPbD7L5UKpvZe9f59ZuM97L9t3jnxRFVWFihUCgUiiwmVk1oUigU\nCoUia/kIlu1VzlWhUCgUHxdqQpNCoVAoFFmMGnNVKBQKhSKLUbOFFQqFQqHIYlTPVaFQKBSKrEXG\nfpj9fjODcq6Kf41KDSrTY2o/NFoNxze7s3vVDoP80tXL0mNqXz4pXYRlQxZycf85g/yc5jlZeHQ5\nlw6dZ92UH4zWW6FBZbpP7YNGq8Fj8xH2rtppkF+qelm6Te1DodKFWTFkMZeS6F3/cBu+dx4DEB4Q\nxpJ+czJrdqpMnr2Yk2cuYpU3D3/+tjpL6gQo3aASn07pidBquLDlGEdXGe6U1aBvS2p2akx8bBzP\nIqLZPHY1T/x1764OWD+eIpVL8PDSXX7sOz9DXc4NqzJo2iA0Wg0HNx1ky8qtBvmm2UwZs3Q0JSqU\nIPpJFLO+mkOwXzBaEy0j5w/HsYIjWq2WI38cZfOKLQC069OWll1aAIIDmw6w86c/021DkQYVaTSt\nO0Kr4a/NHlxcuccgv2q/FlTo3JD42DheRERzaPRaov3DAag/sRNFGzshhMDn9F8cn/prurpqNazO\nqBlD0Wg07Nq0j/XLN6aw99tlkyhdoSRPn0Qx8ctpBPoFJeTbFCzAVo8N/LBoHb+t3gzAN4vHUbdp\nbZ6EPaFT417p6gdwbFCRllN09l7d4sGpVYb21u7bgiqdGuntjWLn2B946p/4bnJ285wMOTKf24cu\ns2/q+uTVJ1C9YTWGTv8ajUbDvk372bhicwpbJ303jpIVShL1JIppg2YQ5BcMQLEyxRg9bwRm5rmQ\n8fEMaPUVb17H8N22ReSzycfrV68BGNV5HJHhkRnabBQfQVhY7eeaDCFEnBDiuhDCUwhxVQhRW59e\nRAjxVxbp8BBCOOv/9hZC3NTrOyyEsM0KHR8aodHQe8ZA5vWczuimQ6jdph4FSzgYyIQFhLF61DLO\n7Er9lfIOo7pw+8Lfmdbbc0Z/FvScybimw6jVph72yfSGB4SydtT3nNt1KkX5N6/eMLnlKCa3HJVl\njhWgXUsXVi+emWX1AQiN4LPpfVjbay7zXEZRuU0dbBwLGsj43/JmsdtEFrQYh+eBC7hN6JqQd3zN\nXjaOWGGULo1Gw+CZXzOpx2T6Nx5Aw7YN+aTEJwYyzTs141nkM3rX68OOH3fSd2IfAOq3rodpdlMG\nugzi65ZDaNm1JTYONhQpVZiWXVowpPUwvmw2iBpNamBfxD5de5vM7MmOnvNZ12QspdrUxKqEoXzI\n39781uobNjSbiNe+izSY2BkA+6olsHcuyQbXCax3GY9txWI41CyTrr1jZ49gWNcxfNGwB65tm1C0\nRGEDmbadWxEVGU37Ol34/YetDJn8pUH+yGlDOHvsgkHa3i0HGdp1TJp6k9vbenovfu01n+UuY6nQ\nphbWya5v4C0f1rhNZmWLCfx94CKuEzob5Dce9TneF+6kq0ej0TBi1lDGdJtAj0Z9aNKuMYWT2dqq\ncwuinz6jS90ebP3hD76c1B8ArVbDN8smsGj8Eno27svQDqOIjUnsVc4YPJu+rgPp6zow6xwr6MLC\nxn4+EMq5puSllNJJSlkJmABk3S9s2jTS67sMpFiGRwihfQ9tyFJdjk4lCPIOJMQ3mLiYWM7tOY2z\nSw0DmTC/EB7f8Ul1Ee6i5YtjmT8PN05ez5Te4k6OBHsHEqrXe37Paaq6VE+mNxTfOz7I9zid39mp\nApYWubO0zk+cHAnzCSLcN4S4mDiu7TlLeVdnA5n7524R8+oNAD7XvMhja5WQ53X2L149f2WUrlJO\npQjwDiTocRCxMbGc2H2C2q61DGRqudbCffsRAE7uO0XlOk6A7pXEHDlzoNFqyJYjG7ExMbx49pxC\njp9w++odXr96TXxcPDcv3KRO89pptsHWqTiR3sE8fRxKfEwcd/ecx9G1qoGM77nbxOrtDbx2H3M7\nK30bJCbZTdGamqDNZorGVMuLsKdp6ipXuQy+3v74Pw4kNiYW911HadCsroFM/WZ12bftIADH9p6g\nWt0qCXkNmtfF/3EAD+95G5S5dsGTqCdRaepNioNTcSJ8gnniG0pcTBw395yndDJ7HyW5vr7X7mOZ\n5PralS+CeX5L7p+6ma6eMpVL4+/tT6De1qO7jlO3meF1qOtam4PbDgNwYt8JquhtrdbAmQe3H/Lg\n1kMAop5EEf8+vldZu1n6v4JyruljATxJniiEyCGE+EXf47wmhGiUQXpOIcRmIcQNIcQWIGca+k4C\njvoyz4QQ04UQF4BaQoiqQogTQogrQohDQgg7vdxQIcQtfd2b9WkN9L3v6/p25BZCNBRC7E1iw3Ih\nRC/9395CiClCiNNAByFEcSHEQb2uU0KI0pk9cXltrQgPTAxPhQeGkzfJFz89hBB0m9ybjbPTDmOl\nrTcfEYHhCccRmdALYJo9G9/umc/UnXOp6lo94wIfkDw2VkQGJNr6NDACS5u0ba3xRSNue2TuYeUt\n+W3zERoQmnAcGhhGPtt8acrEx8XzPPo5FnktOLXvFK9evmLzld/ZeOFXtq/5g+jIZ3jf9aZCjfLk\nzpOb7DmyU61RNaztrdNsg7ltXqIDEpcnjA6MwNwmb5ry5Ts24NFxTwACr97H9+wtBl5ezpeXl+N9\n4iYR9wPSLGttm5/ggJCE4+DAUKztDNtWIIlMXFwcz6KeY2llSY6cOejxVRd+WLQuzfqNIbeNFU+T\nXN+owAgs0rG36hcN8fLQ2SuEoPnkrhya/XuGevLb5ifE4NqGYm2bPxWZt7bG8zzqOZZ5LShUzAGJ\nZOHGufx4cDWdB3U0KDdh8Rh+OryGHsO7ZWxwZvgIeq5qzDUlOYUQ14EcgB3QOBWZrwGklBX0juew\nEKJkOumDgBdSyopCiIrA1TR0twbePmaaAX9JKacIIUyBE0BbKWWoEKIjMAvoA4wHikopXwsh8ujL\njga+llKeEUKYA8Z0T15JKesCCCGOAl9KKb2EEDWAlamdByHEAGAAgLNVJRzNiyTmIVJqMPI+d+nR\nguvHrxARmPl1bVPRmqnFXIbXGkBkyBOsC9kwYdO3+N7xIeRxcKbb8V7IhLFV29WlUMViLO/47T/U\nlVKZTKErdZlSTqWIj4uns3NXcluas+iPRVw9fQ3f+75sXbmNub/P4dWLlzy89ZD4uLQnqohU2pDW\nPVXm0zrYVCzG1i90ofg8hW2wcizI2hpDAfh843i8q5fC/+Jdo3Ultzf19kgGjunDph+28fLFyzRt\nMYbUq0/d4Irt6mBfsRg/d5wBQLXuTfE67klUYMZrJRujJ9XzAWi1WipWK8+All/x6uVrlmxdyN2b\n97h6+hozhswhLCiMnGY5mfnDNJp97sKh7e4ZtscoPoIxV+VcU/JSSukEIISoBWwQQpRPJlMX+B5A\nSnlHCOEDlEwnvT6wTJ9+QwhxI1l9x4UQccANYLI+LQ74Q/93KaA84K6/ybVAoD7vBrBRCPEn8HY2\nyBlgsRBiI7BDSumX6g+BIVv0NpsDtYFtScpkT62AlHItsBagc+F2Bt/GiKBw8tklPv3ms8vHk2Dj\nFkUvUaUUpauVxaV7C3KY5UBrasKr56/YPC/9CShv9VrZJfaorOzyEWmkXoDIEF2gItQ3mDvn/6Jw\n+WL/WecaGRRBHvtEWy3trHgakiLQQsk65XEZ/CnLO35L3Jt/tiZrWGCYQa/S2i4/EcnOa1iQTiYs\nKAyNVoNZbjOiI6Np3K4RlzyuEBcbR2T4U/6+/DclK5Yg6HEQB7cc4uCWQwD0HteLsHQeqKIDI8ht\nn9gzz21nxbNU7P2kbjlqDG7Dli9mJdjr2NyZwGv3iXmhm1zzyMMT+yqOaTrXkMBQbOwLJBzb2Ons\nSkqwXiYkMBStVou5hRlPn0RRrnIZGrdqwJDJX5Lbwpz4eMnr12/Y9suO5GrSJSooAssk19fCzoro\nkJTjlsXqlKPB4Lb83HFmgr2FqpSgcLVSVOvelGy5dN+hNy9e4T5vS4ryoYFhFDC4ttaEBYcnkwml\ngH0BQgPD0Go1mFmYEfUkipDAMK6fv8FTfaj7/LELlCxfgqunryWcr5fPX+L+5zHKOJXOMuf6McwW\nVmHhdJBSngPyA8ljVWl5qvQ8WHr9p0b6cd4eUsq3355XUsq3d5AA/tbLOEkpK0gpXfV5rYAVQFXg\nihDCREo5F+iHLvx8Xt+LjsXweudI1obn+v81QGQSXU5SyrRnfqTBA08vbIvaYV2oAFpTE2q51eWK\n+0Wjyq4YtoQhtfsztO4Afpu1jlM7jhvlWAEeet430FvTrS5X3S8ZVTaXhRkm2XTPm+Z5c1PCuTT+\nXr5Glf0Q+Ho+wLqILVYO1mhNtVR2q83f7lcMZAqWK0KH2f35sd8CnoUbN9aXGnc971KwiD22hWww\nMTWhQZsGnHM/byBzzv08Lp83BaB+q3pcP6MLUYb4h+BUpxIAOXJmp0zl0vje9wMgTz5LAKztranb\nvA7Hd3mk2YYgz4fkKWqLRSFrNKZaSrnV5IG7YRCoQLnCuMzpw599F/Myib3RAWE41CyN0GrQmGhx\nqFmG8HTCwreu3+GTog7YF7LDxNQEl7ZNOHn4jIHMqcNnaNWhOQCNWzfg0mldWwZ8OoS2NTrStkZH\nNv24nXXf/5Zpxwrg7/kQqyK25NFf3wpuNbmT7PralitMm9l92dhvEc+T2PvH8JUsrjOMJXWHc2j2\n73juOJWqYwW4c/0ODkULYlfIFhNTE5q0bcSZw2cNZM4cPkfzDrqfnAatGnD1zDUALp64RPEyxcie\nIztarQanmhXx9vJBq9VgmdcCAK2JltpNa/Lw7qNMn4M0UWHhjxu9U9IC4UCuJFknga7AMX3Y9xPg\nrhHpx/W94IqZbMpdwFoIUUtKeU4fJi4J3AYKSSmP68dLuwDmQoh8UsqbwE1977s0cAUoK4TIjs6x\nNgFOJ1ckpYwSQjwSQnSQUm4Tuu5rRSmlZ2YaHB8Xz7opPzBhw1Q0Wi0eW4/g5+XL5yM78+jGfa4c\nuUSxio6MXDseM0tzqjR1psOIzoxxGZrJU5NS74YpPzJmwxQ0Wg0ntx7F38uX9iM78ejGA64duUTR\nio4MXzsOM0sznJpWo/2IjkxwGU7BEg70nv0lMl4iNIK9q3YS4OX3Tu15y5ipc7l07QaRkVE0adeN\nr/p25zO3Zu9s6x9TfmHgholotBoubD1OkJcfzUd0wPfmQ/4+coU2E7qSPVd2eq0cDsAT/zB+6q/b\nMm/I1mkUKG5PNrMcTD23gs3j1nD3ZPKgSqKu5d+sZPZvs9BoNRzachifez70GNWdeze8OO9+noOb\nDzJu6Vh+OfUz0ZHRzP5aNxdw9/o9jF40irVH1iAEHN7qzqM7uh/ab9Z+g0We3MTGxvH95BU8e/os\nTXtlXDzHvlnPZ7+ORaPV8NeWE4Tf86f2yM8IvvmIB+5XqT+pM6a5cuC2SncfRQeE82ffxdzbd5FC\ntcvR87CuTY88bvDwyLU0dcXFxTF/0lKW/b4QrVbD7s37eXjPm4Fj+nDb8y4nD59h16Z9fLtsEjvO\n/E5UZDSTBk3L8JrNXDmFqrUqk8fKkr2Xt7N20S/s3rQvzXO+b8o6emwYh0ar4erWE4R6+dN4xGf4\n33zE3SNXaTahC9ly5aDjymEAPPUP4/f+izNsh6Gt8Syd/D0Lf5+HRqNh/5YDeN/zoc/oXtz1vMsZ\n93Ps27yfScsm8PvpDURHRjPtK124/dnTZ2xZu521+1cipeT8sYucP3qBHDlzsPD3eZiYmKDRarhy\n6ip7N+7PVLvS5SNYREKkFcP/X0Ufnn077imAiVLKfUKIIsBeKWV5IUQOYDW63mIsMFLv4NJKzwn8\nApQFrqObtDRUSnlZCOENOEspDWJOQohnUkrzJMdO6ELLlugeipYC64Dj+jQB/CalnCuE+B5ohC60\nfAvopR+TnQ+0BbyAN8BuKeW65G0QQhQFVqEbczYFNkspp6d33pKHhd8HJh8o8PK/tJ/r33FZ+PpE\nJmgmPsx+rptjH38QvS2yFXrvOj1igjIW+pc46X80w3Gq9Hg2uq3RvzfmC3e9k65/iuq5JkNKmeqr\nKFJKb3TjnkgpXwG9UpFJK/0l0CmNeoukkW6e7Pg6urHb5NRNniClHJJGnWOBsRm1QUr5CGieWh0K\nhULxwfkIeq7KuSoUCoXio0LGqtnCCoVCoVBkLWo/V4VCoVAoshgVFlYoFAqFIotRzlWhUCgUiqzl\nY3jLRTlXhUKhUHxcqJ6r4n+FPaGZWmMiS6iUt+h71wkf5n1TgPmXZ793nVcrjn7vOgGexv2zZRrf\nlfs5P8yOj7MDPN67znoFyr53nVmFmi2sUCgUCkVW8xH0XNXawgqFQqH4uIjPxMcIhBDNhRB3hRD3\nhRDj05D5Qr+9599CiAz38lM9V4VCoVB8VMgs7LkKIbToNj9xAfyAS0KI3VLKW0lkSgATgDpSyidC\niAKp15aI6rkqFAqF4uMia3fFqQ7cl1I+lFK+ATajW4M9Kf2BFVLKJwBSypCMKlXOVaFQKBQfF1kb\nFi4IJN1b0k+flpSSQEkhxBkhxHkhRIZrr6uwsEKhUCg+KmSs8WFhIcQAYECSpLVSyrVJRVJTkezY\nBCgBNAQcgFNCiPJJ9t9OgXKuCoVCofioyMyYq96Rrk1HxA9IuuefAxCQisx5KWUM8EgIcReds72U\nVqUqLKzIchYsnIrnzeOcv3CASk7lUpVxqlyeCxcP4HnzOAsWTk1Ir1CxDMc8dnD2/D5Ont5FVedK\nAHzRsS3nLxzg/IUDHDm2nfIVyhjUV6NhNTadXM+W07/S7evOKfSZZjNl+qpv2HL6V9buWYGtgw0A\ntg42HLt/gHWH17Lu8FrGzNVtKp7LLGdC2rrDa9l3cyfDvv06XbtLN6jEhKOLmeixlCaD2qTIb9C3\nJePcFzLmwDwGbZxM3oKJe5YOWD+e2Td+ot9PKXYEfCcmz15M/VadaNftyyyt17JhZSqe+p5KZ1Zg\nN/jTNOWsWtWiRsAOzCoWN0jPVjA/zl4bsf0y+dBW+uRrVIk6ZxZT9/xSigxJeY4dejSllsd8ah6d\nS7Xd0zArqYvuCVMt5ZZ+SS2P+dQ6No+8tTP3jmfZBpWYdnQp33osw3VQyjY7Vi/DhL1zWX5/E5Vb\n1DDI+3R8V745vIgpRxbzxdTemdILsGTxdO7cOs3VK+5UdiqfqsyM6eN49OASkRH3DNLr1a3BxQsH\nefXCh/btW6Wpo1pDZ9af+JnfTq+j89cdU+SbZjNlyspJ/HZ6HSv3LMNG//1p+mljfji0OuFz9PEh\nipfVXesl2xay/sTPCXl58uXJtO1pkrVh4UtACSFEUSFENnTbg+5OJvMnuj2yEULkRxcmfphepcq5\npoEQYpJ+yvUNIcR1IUQNIYS3/sQmlz2bQV079XXcF0I81f99XQhRO50626Q1JVyfX0QI8dc/s+7f\nw7VZQ4o7FqFShUYMGTyBpd/NTFVu6XczGTJ4IpUqNKK4YxFcXBsAMHPmBObM/o7aNVsxc8YSZs7U\nnQIfb1+aN+tIzRotmDf3e75fnriggkajYdSsYYzqNp6ujXrTtF1jipQobKCvdecWRD+NpmPd7mz5\nYTtfTUqMEvn7BNDLdQC9XAewYPxSAF48f5mQ1st1AEF+wXjsP5Wm3UIj+Gx6H9b2mss8l1FUblMH\nG0fDYRv/W94sdpvIghbj8DxwAbcJXRPyjq/Zy8YRK4w5xZmiXUsXVi9O/Rr8YzQaiszuz92uM7nR\ncBj52tYjZwmHlGJmObDp25JnV+6lyCs8rTeRx65lUq+gzNw+XO0ylzP1RmH3aZ0E5/mWwB1nONdw\nLOebjMd7xR5KfdsdAIduTQA413AsV76YRalp3UAYt4e20Ag6Te/L8l6zme4ygmpt6mCb7NpGBISx\nYfRKLu06bZBerEpJijuXYmbz0cxwHUXhSsUpUdN4x96ieWNKOBaldNm6DBo0jhXL56Qqt3evO7Xq\npHSej3396dtvBJs2/5mmDo1Gw7CZQxjffSK9GvWjSdtGFC7xiYFMy07NiX76jG51e7Hthx0MnNgP\ngCM7j9G/2Zf0b/Yls4fNJcg3mAe3HiSUmzVkbkJ+ZHiaEdRMI+ON/2RYl5SxwGDgEHAb2Cql/FsI\nMV0I8fYJ7hAQLoS4BRwHxkgpw9OrVznXVBBC1AJaA1WklBWBphgOeBsgpaydXn1Syk+llE5AP+CU\nlNJJ/0nTKUspd0sp5/4zCz4crVu7sGnjDgAuXbqOpaUFNrbWBjI2ttZY5Dbn4kXdj+umjTtwc3MF\ndGuGWuTW7RNvaZGbwMBgAC5cuEpkZJSu3ovXKFgwcSWdMpVL4+ftT8DjQGJjYjm66xj1mhleknqu\nddi/7TAAHvtOULVuFaNtcihakLz58+B54UaaMp84ORLmE0S4bwhxMXFc23OW8q7OBjL3z90i5tUb\nAHyueZHH1iohz+vsX7x6/sroNhmLs1MFLC1yZ2md5pUdeeUdyOvHwciYWCJ2nSZvs+op5BzGdiFw\n5Z/Ev35jkJ63eXVePQ7m5b00v1KpYlnFkRePgnjpE4KMiSPoz7MUaG54juOevUz4W5sre8LImVnJ\ngkSc0j2LvgmLIibqBRZOxYzSW8TJkVCfIML01/bynrNUcq1mIBPhF4r/nccp1ryVSEyzZ8PE1AST\nbKZoTbREhz412mY3t2b8unE7ABcuXsUyjyW2tinfArlw8SpBQSknsPr4+HHz5m3i09mirbRTKQK8\nAwh8HERsTCzHdnlQx9Xw+1PHtTaH9N+fE/tOUqVu5RT1NGnbmGO7jhtt2zuRxe+5Sin3SylLSimL\nSyln6dOmSCl36/+WUsqRUsqyUsoKUsrNGdWpnGvq2AFhUsrXAFLKMCllQgxeCJFTCHFQCNFff/xM\n/39DIYSHEGK7EOKOEGKjEEY9Hg8RQlwVQtwUQpTW19VLCLFc/7eNvvfrqf8Y3PlCiGJCiGtCiGr6\ncjv07fMSQsxPIucqhPg/9s47vsbrf+Dvc69ESAghMhF7RxB7j9i7pZSaVdqitWsratRoa1eH8W2t\nttQsYsTeI/YWZC8RsXPv+f1xr+TezBuC5tfzfr0uec75nPM55znneT7P2UeMuv4QQtgZ3WcYF0ef\nE0LMNrp1EkJcMOrbb6mZNI0AACAASURBVPGNc3UiMDAk4To4KARXV/Mt5VxdnQkKSpQJCgrFxdXQ\nzTRq5GSmThvNlWuH+Gb6GCZOmJVMR4+eH7Bz576Ea0fn/IQHJ75YwkMicUxi0E1ldDo9j2IfYZ83\ntyHNhZxZtuNHFvz5HRWrVUimz6ddI3Zv8ksz33mcHIgJTvyQfRASjb2TQ6ry1Ts35LLf2TTj/Ldi\n7ZyP5yZ5fR4ShZWLeV5zli9Cdtd8xOw6ZeauyZEdl886EDRnXYb12jg78NRE79PgaLI7J7/HBXs3\npc6xHyg5vhtXxi4H4OGluzg290ZoNeQo5EhuzyLYuOazSG8eJwfum+i9HxJFnjTK1pTbp69z9chF\nZpxYyszjS7m035/Qm0EWhQVwc3Um8F7i8F9QYAhurpm7RWN+l/yEh0QkXEeERpLfxbwzLb9zvgQZ\nvU5PXOwjchufn5c0aFOf3UmM66i5w/lpxxI++qIbmYmMt/z3rlDGNWV2AgWFENeEEIuEEPVN/OyA\nzcAqKeVPKYStBHwJlAWKArUt0BcppawMLAZS2sx1HrBPSlkRqAxcfOkhhCgF/AX0llK+HFz3Aj4A\nKgAfCCEKGruexwFNjLpOAkOFEA5AB6CcsZX+sg9xAtDMqDP54JZB9ydCiJNCiJMv4h++dEsml/Rr\nPmUZw/8f9+vOVyOnUrpkbb4aOZVFi80b7/Xq1aBnz85MGJfo/so6gajwaDpW60rvZv2Z//UiJi4c\nS067nGZyjds1ZNffu5OFN1eQglsqJ3dUaV+Hgp5F2bN0c9px/ltJb26lEBSe1Js7Xy9PJuY+oguh\nP21G//gVWukpfqYmv8f3lu3kYPUvuDZ1FUWHGMaDg1ft5VlINNV3TqPUlJ7EnLiG1OksU2tB/UoN\nx8JOOBd3Y0yNAYyu0Z9StcpTvFqZ9ANmgm6LdaRwYy15fkzrd5lKpXn29BkBVwMS3L4ZNJ2+TT5h\ncMchVKhWgabvNcm0NGdmt/CbQhnXFJBSxgFVMEzfjgDWCiF6Gb03AsuklCtTCX5cShkopdQDZwEP\nC1SuN/5/KhX5RhgML1JKnZTyZb+SozE93aWUps2g3VLKB1LKp8AloDBQA4PBPySEOAv0NLrHAk+B\nn4UQHYHHxjgOAcuNrXNtSomWUi6VUnpLKZedOLmfw0e3EhISjru7S4KMq5tLQtfuS4KCQnBzS5Rx\nc3Mm1CjzYbeObNy43XBT1m9NmNAEUK58aRYsmsEHnT8hOjpx/CY8JIICroldZQVc8hMZFmmm01RG\nq9Vgm9uW2PuxvHj+gtj7hu7mq+evExQQTKGiieOHxcsWRZtNy9Xz11O6BQnEhEaTx6QlZO/iwIPw\n+8nkStYuj8/ADvzy8Sx0z9/hZ/Vr8DwkCmuTvFq75ONFaHTCtdYuBzlKF6LsX1PwOrYEu8olKbl8\nNLaexbCtVIJC43rgdWwJzh+3xm1QR5x6t7BI79OQaLPWpo2rA89Ck9/jl4RuOIxjC0P3rdTpuTph\nJUcbf8XZnrOxsrfl8a1Qi/TeD40ir4nevC75UizblPBqVo3bZ67z7PEznj1+xkW/MxSpVCLNMJ8O\n6MnJEzs5eWInwSGhuBd0TfBzc3chOMnz9LpEhERQwCWxp8fROT9RoVFJZCITZDRaDXa5bYmNeZjg\n37BtA/b8bd5qjTTG8eTRE3b/vYfSlUpnWpqVcc3CGI2Yn5RyIobB7veMXoeAFml09z4z+VuHZcud\nXoaxVP4lDzCMBSdtHaeUBgH4moz3lpVS9jUO5lfD0PptD2wHkFIOwNDSLQicFUKk1Ye2sFaNVtSq\n0Yotm3fStVtHAKpW9SI29iFhoRFmwmGhETyMi6NqVS8AunbryJYtvgCEhoRTt65htmWDBrW4eTMA\nAHd3V1atXky/vkO5ceO2WXxXzl7BvYgbLgWdyWaVjcbtGnFw5xEzmYM7D9Oyk2Fct0Gr+pw6ZBjv\nzeNgj0ZjeAxcC7lQsIg7QXcTu6ybtGvMrr/3pJF1A/f8b+Lo4YyDuyNaKy2V2tTioq95l6hbOQ86\nTevHzx/PIi4qNt04/63Enb2BTREXshcsgLDKhkO7OtzfmbgiQffwMafL9+Js9QGcrT6AuNPXuNZr\nOo/O3eRyh3EJ7qE/byFo/nrClv1jkd7YMzfJWdSZHIUcEVZanNvXInyH+T3OWSSxy9TRpxKPbxnK\nUpPD2jAGCzjUq4CM1/HommXds3f8b1LAw4V8xrL1blOLc74nLQobHRxJyepl0Gg1aLJpKVG9LKE3\n0ta7eMkKvKs2xbtqUzZt2sFH3d4HoHq1ysQ+iE1xbPV1uOJ/Fbcibjgbn59G7Rpw2Nf8+Tnse4Rm\nxuenfqt6nDmU+C0vhKBB63rs2ZRoXDVaTUK3sTablppNqnP7SkCmpTkrGFe1zjUFjF2teinly+aK\nF3AHQzfrBGA8sAj49C0labdR1/fGfTBtje7PMRjEHUKIOCllWptJHwUWCiGKSylvCCFykrieK6eU\ncpsQ4ihwA0AIUUxKeQw4JoRog8HIpjk7DmDH9r00a9aQcxf8ePL4CQMGJC4tOXx0K7VqGGY0fvnF\neH78cRY2OWzw3bmPnTv8ABj4+Wi+nT2BbNpsPH32jEEDDce7fTVmMA4OefnuhykAxMfHM6iNYdmM\nTqfnu3HzmbtqJlqNli1r/+H2tQA+Ht6LK/7XOOh7mC1rtjF+3hjWHvwfsTEPmfiZIR6vGp58PLw3\n8Todep2eWaO/46HJF3mjNvUZ/tHo9LKNXqfnrwnL6L9yDBqthmPr9hJ6PZDmQzpx7/wtLu46RdvR\n3cieMzu9FhnSfT8okl/6zQZg0LpJFCjmirWtDROPLGTNqB+5uj/1CVSWMmLiDE6cOUdMTCyN23fn\ns74f8V6bZq8XqU5PwNifKbVqAkKrIWLNbp5cu4fbiC488r9JzM5Ul/69FlKn58roZVReMwah1RC0\nei+PrgZSbGQnYv1vEbHjFAX7NiNf3fLo43XEP3jEhcGLAbDOb0+VNaOResmz0GjOD7R8ZrZep2fN\nhF8ZtHIsGq2Gw+v2EnI9kNZDOnP3/E3O7TpFYc9i9P9xODntbanQuAqth3RmStNhnN52lFK1yjNu\nx2yQcHHfWc7vPpW+UiPb/tlN8+aNuHr5EI+fPOHjj4cm+J08sRPvqgaDN2P6WLp80IGcOXMQcOsk\nvy5bxeQpc/GuUpE///iFvHntad3Kh4kThlHRq1Gy/M0bv4Bvf5+ORqPhn7U7CLh2h97De3LV/xqH\nfY+wdc0/jPnhK347uJzYmIdM+eybhPCeNSoQERJJyN3EngBra2tm/T4drVU2tBoNpw6eYeuqbRbn\nO12kZTO93yUiK5zo/rYRQlQB5gN5gHgMBucTDOOU3hiMzK9AhJRypNGw2QkhGgDDpZStjfEsAE5K\nKZcbr838jW4BgLeUMlII4Q3MllI2MHZDe0spBwohnDAsgi6KoSX6KRACbJFSlhdC5AF8MYyX5n0Z\nzhj/FmOcfkKIRsBMILtR/TgMa7w2AjYYWrezpZQrhBDrMSySFhiM+5cyjcpil7PIW69I7+o812pW\n6e7Z/Ub4b53nav1O9G7I8W6aOj8FH3rrOt/lea57A31fyzqG1mtg8fvGeb/fO7HEyrgqMgVlXN88\nyri+eZRxfTu8rnENrtXQ4veN6+G978S4qm5hhUKhUGQpZBboFlbGVaFQKBRZinc5UclSlHFVKBQK\nRZZC6lXLVaFQKBSKTCUrTBVSxlWhUCgUWQrVclUoFAqFIpPR65RxVfxH0Gre/mZf/XFNX+gN8Jsu\nMn2hN8C7WBZT+dzst64TYJh3+ht3/H/CMaf9W9eZX5szfaF/KarlqlAoFApFJqOW4igUCoVCkcmo\npTgKhUKhUGQyetVyVSgUCoUic9Hr/v0HuinjqlAoFIoshVrnqlAoFApFJqNmCysUCoVCkclkhTHX\nf3/HtSLLMXPWBM747+HQ0a1UrFguRRkvr/IcPraNM/57mDlrQoL7shXzOHB4MwcOb+bcxX0cOLzZ\nLJy7uwtBoecYNPjjVPW7NvCk3f5ZtD84h/Kft0nmX/KjRrTZNZ3WO7+h+Ybx2JcwrJfN51WU1ju/\nMfx8v6Fgc+908+rdoAq/+P3MsgO/8sFnnZP5W1lbMWbRaJYd+JV5m77Hyd0JAG02LSPmDuNH38X8\nvGcpXT7/ICFM+z7tWLprCUt3/UiHvu3TTYN9g0p4HphPxUMLcRnYIVU5h1Y1qR68HlvPYmbu1m75\n8b7+O84D2qWry1LGTZtLvVZdaN99QKbFmZQy9Ssydvd3jPf7gSafJk97w76tGOM7h1H/fMvnv48j\nr1v+V9ZVtn5FJu3+nq/95tE0BV3Fq5Vh9JYZLLixmkotqpv5tf+qG+N3zGb8jtlUaV3TIn1TZo7h\n8Ont7D60gQoVy6Qo41mxLHsO/c3h09uZMnNMMv8BA3sTEnMJB4c8AHTs1Jrdhzaw+9AGNu34ncJl\nPFKM16t+JX7Ys4j5+5bQ/tP3kvmXqVaWmVvnsubmemq0rJXgnt/NkZlb5jBr23fM9Z2PT7fmFuX1\nVZBSWPx7VyjjmoUQQsRlcnweQogLxr+9hRDzXjdOn6YNKFbMg0oVG/HFoLHM/X5yinJzv5/MF4PG\nUqliI4oV86CJT30AevccTN1abahbqw2bNm5n86YdZuGmzxzHLt99qedJI6j+TU92d/+WTQ1H4tG+\nRoLxfMntDUfY3GQ0W5qO5cKirXhP7A5AzJVAtrYYz5amY9ndbRY1ZvZGaFN/RDQaDQOnfs7YHuPo\n1+gTGrRrQKEShcxkmndpRlxMHL3r9mH9zxvoO6YPAPVa18UquxX9fT7l85aDaNmtJU7uTniUKkzL\nD1swqPUXDGj2KdUbV8fVI43NMjQaPKb142q3qZxr8AX52tUlRwn35GK2Njj1bUncqWvJ/ApP6k3M\nnjOp63gF2rf0YcncqZkapylCI+g0uQ9Lek1nms9QqrStjXNxNzOZwEsBzGozmpktRuL/zzHaje72\nyrq6TO7Lgl7TmOwzhKop6IoOjmTl8EWc2HjQzL18w0oUKleEb1qOZGb7sfh80hYbuxxp6mvkU4+i\nRQtTq3JzRnwxkRlzJqYoN2PuBEZ8OZFalZtTtGhhGjWpm+Dn6uZM/YY1CbwXnOB2904gHVv2pHHt\nDnw/awn9p3+eLE6NRkPfKf35pufXDGkykNpt6+JeoqCZTGRwJAuH/cDBjfvN3GPC7zO24yhGtBzC\nmHYjaP9pR/IWcEgzr6+KlJb/3hXKuCoAkFKelFIOft14WrVuwurVGwA4eeIs9va5cXJyNJNxcnIk\nV247Thw3vNBXr95A6zY+yeLq0LEVf/6xxSRuHwJu3+Py5eup6s9XqRgPA8KIuxuB/oWOgI1HKdis\nipnMi7gnCX9ny5k94QnUPX2O1BkW0GmzW0E6D2Ypr1IEB4QQejeU+Bfx7Nu0j1pNzVsmNZvWxPfP\nXQDs33qASrW9AINKmxw2aLQarG2siX/xgsdxjyhYvBCXT1/h2dNn6HV6zh87T+3mtZLpfoldpeI8\nDQjh2d0w5It4ojceJG+zasnk3Ed+SMiiv9E/e27mnrd5NZ7eDePJtXtpZzaDeHtVwD53rkyN05TC\nXsWJuBNG1L1wdC90nN58mApNq5rJXD9ykRdPDfkNOHOdPM75XkmXh1dxIu6EEmnUdXLzYSom0RUd\nGEHQlbvIJG9zlxLuXD92Cb1Oz/Mnzwi8fIey9b3S1Ne8ZSP+WLMRgNMnz5HbPhcFnMxb3QWc8pMr\nlx2nTvgD8MeajTRv1TjB/+tpo5gycY5Zek4eP8uDB7EAnDrhTz6X5PejuFcJQgNCCb8XRvyLeA5t\nPoC3j3l9iggM5+6VO0i9+WLT+BfxxD+PByCbtRWaN7hrm06vsfj3rlDGNQsihGgghPATQvwphLgi\nhPhdCCGMfjOEEJeEEOeEELONbsuFEO+bhE/WAjbGucX49yQhxK9GHbeEEBYbXRcXJ4ICE7+Wg4ND\ncXV1NpNxdXUmOCg0USYoBBcXJzOZWrWrEhEeya2bAQDkzJmDL4d8wozpaTeuczrn5VFwdML145Bo\ncjrnTSZXqmcTOhyaQ5VxXTg+YWWCe/5KxWi7ZwZtdk/n6FfLEoxtSuR3zkdEcETCdURIJPmSvMBN\nZfQ6PY8ePiJ33twc2HqAp0+esubUKn4/9j/+/PEvHsbEEXA1gArVy5MrTy6y22SnasOqOLqaf5yY\nYu2cj+fBUQnXz0OisHIxby3kLF+E7K75iNl1ysxdkyM7Lp91IGjOulTj/7eSx8mBGJN8x4REYe+U\nvJxfUqNzQy75nX1lXfdNdN0PiSKPk2UtssDLdyjXwAsrG2ts8+aiVM1y5E3BqJni7FLA7PkICQ5L\n9ny4uDgRHBxmJuPsUgCApi0aEhoSzqULV1PV0fWj9zjjdzqZu4NzPqJCErf3jA6JSlan0yKfS35m\nb/+BJUd/4e8l67kfHp1+oFcgK7Rc1YSmrEsloBwQDBwCagshLgEdgNJSSimEyPMa8ZcGGgK5gKtC\niMVSyhemAkKIT4BPAGys82NtlRujjTcj6dd8CiLJZN7v1IY//0gcbx0z9ksWLVzGo0eP00x0SvpT\naoFeXbGLqyt2UaR9TTy/aM+hL38EIPLMTTY1+gr74q7U/r4/QXv90T97kTyCVDKSNB+Qskwpr1Lo\ndXq6encjl70dc/6aw+mDZ7h34x7rFv3BjFXTefr4Cbcu3UKv06WR4RTcTJMgBIUn9ebml/OTibmP\n6ELoT5vRP36aevz/VlK89ymLerevQyHPYsz7YNIrqrKknFPm8oFzFPYsxoj1U4mLiuXW6Wtpl6el\n+lKSQZIjhw1fDOtPl46pz0moVbcaH37UkWmdU+5uTld3GkSFRDK8+RfkLeDAyJ9Gc3TbIR5EPrA4\nvKVkhQlNyrhmXY5LKQMBhBBnAQ/gKPAU+FkIsRXYknrwdNkqpXwGPBNChANOQKCpgJRyKbAU+Pzc\nuUtVAM6cOo+buytgaCW5ujoTEhJmFnFQUCiubomtWVc3F0JDwxOutVotbdo2o36dxIkjVapWpG37\n5nw9ZRT29rmRej3XxEauLvc1i/tRSDS2romtipwuDjwOu59qJm9vPEr16b2TuT+4EUz8k2fkLeVO\n1LnbKYaNDIk0a1U6uuQnOsz8Sz0y1CATGRqJRqvBNpctD2Me0qh9Q074nUIXryMm6gEXT16kpGcJ\nQu+Gsn3tDravNYw19x7Vi8iQ1A8KeB4ShbVrYsvC2iUfL0IT06C1y0GO0oUo+9cUAKwc81By+Wiu\n9ZqObaUSOLSqSaFxPdDmtgW9HvnsOWHL/klV37+FmNAo8pjkO49LPmLDk5dzydoVaDqwI/M+mJTQ\nZZlR7odGkddEV16XfDxIQVdqbF+4ge0LDUMlfX4YTPjt0GQyvT7uSreenQDwP33e7PlwcXUyez4A\nQoJDcXV1MpMJC4mgcJGCFCrsxu6DGxLcd+77ixaNPyAiPJIy5UoyZ95kur3fH9cY62TpiA6NIp9L\nYhe0g0u+ZHXaEu6HR3Pv2j3KVCvH0W2HMxw+PbLC3sKqWzjr8szkbx2QTUoZD1QD/gLaA9uN/vEY\ny9rYfZz8qbIg/jRkF76chLRly066djXMWPWu6kVs7EPCwiLMhMPCIoh7+Ajvqoaxp65dO7B1y64E\n/wYNa3Pt2k2CgxNfQi2adsGzXH08y9Vn8aJlzJm9OJlhBYg6e4tcRZyxK+iIxkqLR7sa3Ntp3v2V\nq0jiS8m9iRexxpedXUHHhAlMtm75yF3Uhbh75mk35ar/Vdw8XHEu6EQ2q2zUb1ufI75HzWSO+B7F\n5/0mANRrVZezhwxjZOFB4XjVrgiATY7slKlUmns3DN8uefIZTkhxdHWkTvPa7N3ol2oa4s7ewKaI\nC9kLFkBYZcOhXR3u7zyR4K97+JjT5XtxtvoAzlYfQNzpa1zrNZ1H525yucO4BPfQn7cQNH99ljCs\nAHf9b+Lo4YyDuyNaKy2V29TivO9JMxn3ch50mfYxP338LXFRsa+s647/TQp4uJDPqMu7TS3OJdGV\nGkIjsM1jB4Bb6UK4lS7E5QP+yeSW/7wan7od8anbkX+27qZTF8OHZWVvTx7GPiQ8zPwDKzwskri4\nR1T29gSgU5d2bN+2hyuXrlOhRF2qefpQzdOHkOAwmtZ/j4jwSNzcXfjlf/MY1P8rbt28k2J6b/hf\nx6WICwUKFiCbVTZqt6nLSd/jFuXVwTkf1tkNrxbb3LaU8i5N8M0gi8JmFL0UFv/eFarl+v8IIYQd\nkFNKuU0IcRS4YfQKAKoA64B2gNWbSsPOHX40bdaAs+f28PjJUz4fMCrB78DhzdStZVgaM/TLCSz6\n8Vty2GTH13cfvjv9EuTee781f/2xOWnUFiF1eo6PW0GTVSMRGg031u7jwbUgKg5/jyj/2wT6nqZ0\nr6a41C2HPl7H8wePErqEC1QrSfnP26CP1yH1kmNjlvPsfuoTtPU6PQvGL2Lab9+g0WrYsXYnd67d\nocewj7h27jpHfY+yfc12Rn0/kmUHfuVhzEOmfT4dgE0rNjN8zjCW7voRIWDnOl9uXzG0kMcvHU/u\nPLmIj9cxf9xC4h6kMUlcpydg7M+UWjUBodUQsWY3T67dw21EFx753yTGxNC+TUZMnMGJM+eIiYml\ncfvufNb3I95r0yzT4tfr9Pw54Vc+WzkGjVbD0XV+hF4PpOWQTtw9f4sLu07RbnR3rHPa0HvREADu\nB0XyU79Zr6RrzYRfGbRyLBqthsPr9hJyPZDWQzpz9/xNzu06RWHPYvT/cTg57W2p0LgKrYd0ZkrT\nYWitsjHsD8OM+adxj1k2ZD76NMbxAXbv3E9jn3ocObOdJ4+fMuTzsQl+vgfW41O3IwBfDZ3M94um\nYZMjO3t8D7DHd39qUQIwZOSn5HWwZ/ocw9K3HDIbX7UZliyvv0xYytiVk9BoNexdt5vA6/f4YOiH\n3Dx3g5O7jlPMszgjlo7G1t6OKk2q0nlIV4b6DMK9uDs9xvVBSokQgs1L/+bu1ZSN+OuSBTZoQmSk\nP13xbhFCxEkp7YQQDYDhUsrWRvcFwElgB7ARsMEwGjdbSrlCCOFkdNcAu4FBxng8gC1SyvKmcQoh\nJgFxUsqXE6IuAK2llAGppc3erthbr0jz7S1bM5jZ/KZ5N+e5TtG9/fM3/2vnub54R6/tv2MuvHWd\ndexLvHWdL/njzsbXalIecn7f4oKqHfrnO2m+qpZrFkJKaWf83w/wM3EfaCKWbB2GlDIMqGHiNNro\nHgCUTxqnlHJSkvDlXzftCoVCkVlk9olzQojmwA+AFvhZSjkjFbn3gT+AqlLKNMcG1JirQqFQKLIU\nEmHxLz2EEFpgIdACKAt0FUKUTUEuFzAYOGZJGpVxVSgUCkWWQi8t/1lANeCGlPKWlPI5sAbD3JSk\nTAG+xbAiI12UcVUoFApFlkKPsPhnAW6A6RZlgUa3BIQQlYCCUkqLlzeqMVeFQqFQZCl0lhlNwHyz\nGyNLjWv0E0RSCJbQ5hVCaIDvgF4ZSaMyrgqFQqHIUlgylpogm7jZTWoEAqanE7hj2PnuJbkwTPz0\nM+6e5QxsEkK0TWtSkzKuCoVCochSZPJs4RNACSFEESAI6AJ8+NJTSvkASNi2Sgjhh2HZYpqzhZVx\nVWQKhe0KvHWdF6zS3qP1TdFM/+rngr4OD3Svtn3f6/Cu1pvOOTn9nejtUWXoO9HrluPVTux5Herp\n39ypRW+azDSuUsp4IcRADPsEaIFfpZQXhRCTgZNSyk2vEq8yrgqFQqHIUmSkW9ii+KTcBmxL4jYh\nFdkGlsSpjKtCoVAoshT6f/++/cq4KhQKhSJrkZHZwu8KZVwVCoVCkaXI7O0P3wTKuCoUCoUiS6FP\n4bD4fxvKuCoUCoUiS5EVznJTxlWhUCgUWQrVLaz4z1G7YQ1GTfkSjVbL+t838euC/5n5W1lb8c38\nCZT1LM2D+w8Y0X8cwfdCsc+bmzk/T6O8Vxk2rt3G9DFzEsK0aO/Dx1/0REpJRGgkowdOIib6Qapp\nKFm/Iu0m9EBoNRxfuxe/xebL1Or2bUm1Lg3Rx+uJi47lj5E/EhMUiUvZwnSc2ofsdjmROj17Fm7A\nf8tRi/PuUd+ThpM+Qmg1XFjjx/FF5ge+V/m4BRW6NkAfr+Nx9EN2DF/Kw6AoAOqN6UKRRl4IIbhz\n8AJ7J/4vJRUpkq9hRUpP7YnQagj8fQ8B883z696jCQX7NEXq9OgePeXS8J94dC0IYaWl7Kx+5PYq\nCnrJlXEruH/4ksV6X1KmfkU6TuiFRqvhyNo97Fq80cy/Yd9W1OzSCF28jrjoWFaNXML9oMw/E3fc\ntLnsP3Qch7x5+Pu3JZkad8X6legx8WPDAeJrfNm0eL2Zf+lqZekxsS+FSnswb9Bsjm87Yuafwy4H\ns3cv4MSOoyyf8FOqemo2rMbwyV+g0Wr4e9UWViz43czfytqKr+eNpYxnKR7cj2V0/4mEBIZSzqsM\nY2aNAEAIwdI5v+L3zwEA7HLbMX7OKIqVLoKUkslDZnD+1MVU01CogSf1jPX40mo/TiWpx179WlCu\nSwP0Oh1Poh6y26Qe1xr9AR6NvQA48cPfXN9s0QEyGSY+C3QLq437/wMIIXRCiLNCCH8hxGkhRC2j\nu4cQQgohppjI5hdCvDAewI4QYpIQYrglejQaDWOmD+PTD4fSvl5XWnTwoWhJDzOZjh+2ITbmIa1r\nduJ/P67hy3GfA/D82XMWzlzKnK8XmMlrtVpGTf2Svu99zvuNPuLa5Rt07fN+6nnVCDpM7s0vvWYy\nx2c4Xm1rUaC42R7cBF8KYF6bsXzXYhTn/zlGq9GGzVhePHnG2qGLmdt0BL/0nEGbCT2wyW3ZAeVC\nI2g8tSfre37L8sYjKdW2Bg4lXM1kwi8G8Fur8axsNobrW49Tf0xXAFyrlMDVuyQrm45mhc9XOHsW\nxb1GGYv0ohGUqzjN5AAAIABJREFUmdGH0x/O4FDdYbh0qI1tSfP8hqw/xJEGIzna+CsCFm6m1Ncf\nAeDevTEARxqM5FTnbyg1qTtk8KUlNIJOk/uwpNd0pvkMpUrb2jgnud+BlwKY1WY0M1uMxP+fY7Qb\n3S1DOiylfUsflsydmunxCo2G3lP6M7PnZIY3GUSttnVxK+FuJhMZHMmSYfM4tHF/inF0GvYhl4+l\nbtDA8PyMmjaUwd2G06n+RzRr34QiSZ6fdl1b8fDBQzrU6sqqpesYNG4AADeu3qJH83508+nDoA+H\nM+bbEWi1WgCGTxnM4b3HeL9ud7o27s3t63fSyKugwdSebOrxLb83GknJdjXIm6QeR1wIYG2r8axu\nOoYb245Te6yhHns08sKxvAerm41lXZtJVBrQCiu7HGnm+VWRGfi9K5Rx/W/wRErpJaWsiOGgdNPt\nb24BrU2uOwFpvwVSoXylsty9HUjQ3WDiX8Sz/e9dNGxWz0ymQbO6bFpnWKvtu2Uv1et4GxL4+Cln\njp/j2bNnZvJCGP7JkdPwkNra2RIemnqrp6BXcSLvhBJ9LxzdCx3+m49Qrqm3mczNI5d48fQ5AHfP\n3MDe2QGAyNuhRAaEAhAbfp+4qFjsHHJblHdnr2LEBITx4G4E+hc6rm4+SvGmVcxk7h25TLxRb8iZ\nG9i5GPRKKcmW3QqtVTa01lZorLQ8jky9ZW6KfeXiPL4dypM74cgXOkL/PkyB5ub51cU9SfhbmzN7\nwhvHtqQb0QcuAPA8MpYXsY8NrdgMUNirOBF3wogy3u/Tmw9ToWlVM5nrRy4m3O+AM9fJ4/xmdiPy\n9qqAfe7M33WouFcJQgNCCL8Xhu5FPEc2H8Tbp7qZTGRgOHev3EGmcMZZkfLFsM+fh3P7z6app1yl\nMtwLCCLobgjxL+LZuXE39ZvVMZOp37wuW9ZtB2D3Fj+q1TXUsWdPnqHTGXYsy57dGikN6bC1y0ml\nGhXZuMpwmEv8i3jiYuNSTYOTsR7HGuvxtU1HKZqkHgeZ1OPQ0zewNT4/eUu4EXTsClKnJ/7JMyIv\n3aVwA8808/yq6IXlv3eFMq7/PXID902unwCXhRAv38gfAOteJWInF0fCgsMTrsNCwing4piCTBgA\nOp2OuIdx5HGwTzXO+Hgd34yaxV97f2O3/2aKlfRgw6rNqcrbO+XlQXBUwvWDkChyO+VNVb5q5wZc\n8fNP5l6wYjG0VtmIuhOWalhT7Jzz8jA4OuH6YUg0dmnoLf9BfW7vNegNOX2De4cv0f/kAgacXEDA\nvvNE3whONawpNs4OPDXJ79PgaLIbX3Zm+endlDrHfqDk+G5cGbvckMZLd3Fs7o3QashRyJHcnkWw\ncc2Y4cvj5ECMif6YkCjs08h3jc4NueSXtpH5t5HX2YGokMQPuqiQKPKmcI9TQghB93G9+X3ainRl\nCzg7EhaU+PyEh0RQwDl/Epn8Cc+YTqcjLvYR9sbnp1ylsqz1W8mavcuZPmo2Op0Ot8KuxETFMPH7\nMfy+8xfGzR6FTQ6bVNNg65yXOJN6HBcSjZ1z6uVZrkt97hifn8jLdyjcoCLZbKyxyWuHe82y5HK1\n7D5lFH0Gfu8KZVz/G+QwdgtfAX7GcOivKWuALkIId0CH+YkQlpNCl+LLL+gMyZiQLZuWzj070rlJ\nTxpXbMO1yzfpO7hHhtKQWt9QpfZ1cPcsyr6l5sY6l2Meusz9jD9GLEkzbeZqLddbpkNtnDyLcvLH\nrQDkKeyEQ3E3llYfzI/VBlGoVlncqpWySG/Ka+mTK763bCcHq3/BtamrKDqkAwDBq/byLCSa6jun\nUWpKT2JOXEPqMrhfc4rlmbKod/s6FPIsxp6lr7RV6ztDpHSTLexv9OnRgrN7TxEdYsEYc0pqkupJ\nsZ4ZhC6eucQHDXrQo8Un9B7UHevs1mizaSlVoSR/rvibbk378uTJE3oNSr1bPqV6nFp5lupQmwKe\nRTm9xFCP7+2/wJ29Z3n/74k0W/A5oaevo49/M+YtK3QLqwlN/w2eSCm9AIQQNYGVQojyJv7bMRjc\nMGCtpZGanpPolqsIYcHhOLkmbuDv5FKAiCRduAYZJ8JCItBqtdjlsuPB/dhUdZQqXxKAwDtBAOzc\ntJs+gz5KVf5BaDT2Jq0ve5d8xIbfTyZXvHZ5Gg1sz5IPJqN7nrghfna7HPRZNpLtc9Zx98yNtLJv\nxsOQaLOv9FwuDsSloLdQnXJUH9iWtZ2/SdBbvLk3IWdu8OKxoUv8tp8/rpWLE3T8arp6n4ZEm7U2\nbVwdeBaaXO9LQjccpszMvsBipE7P1QkrE/yqbZnM41uh6eo0JSY0ijwm+vOkcr9L1q5A04EdmffB\nJOKfv/0DCF6H6NAo8rkktiDzueTjflh0GiESKVG5FKWrlsXnoxbY2NqgtcrG00dPWTMz+YS18JAI\nnNwSn58CLo5EhEUml3EtQPjL5ye3bbLnJ+D6HZ48fkqx0kUID44gPCSCi2cME9V2b/Gj18DuqaY3\nLiQaO5N6bOfiwKOw5OVZsE45vAe1ZX2nb9CblOfJ+Zs4aZxQ13T+Z8Tczlh9spSssP2harn+x5BS\nHsFwfJKjidtz4BQwDPgrA3EtlVJ6Sym9HXI6cfHsZQoXLYhbIReyWWWjefsm+O08YBbGb+dB2nZu\nCYBP64YcP3QqTR3hIREULelB3nx5AKhRrxq3rgekKh/of5P8Hs7kdXdEa6WlYpuaXPI11+FazoP3\npn3Mio9n8ygq8cWktdLS48ehnFp/gPPbMjbLMdT/FnmKOJO7oCMaKy2l2tTgpu9pM5kC5QrjM70P\nf/edyxMTvQ+DI3GvURqh1aDJpsW9RhmiLOwWjj1zk5xFnclRyBFhpcW5fS3Cd5jnN2cR54S/HX0q\n8fhWCACaHNaGMVjAoV4FZLyOR9eCMpTvu/43cfRwxsF4vyu3qcV5X/OTuNzLedBl2sf89PG3xEWl\n/iH1b+Wm/3Wci7jgWLAAWqts1GxTh1O+xy0Ku/CL7xhUqx+D63zCb98s58D6vSkaVoBLZ69QsIg7\nrgUNz0/Tdo3Zv+Ogmcz+HQdp3bk5AI1bN+DEQUMdcy3okjCBydndicLFChF8L5SoiGjCgsMpXMxw\nXGm1OlW4dS0g1fSG+d8ij0diPS7Ztga3k9Tj/OUK03BGH7b0Ma/HQiOwyWMHQL7SBclfpiB395+3\n6D5llPgM/N4VquX6H0MIURrDsUpRgOlU2DnAPillVIpdnBag0+mYNmYOi1d/j1ar4e/VW7h59Taf\njezHpbOX8dt5kA2rNjNtwUS2HPmDBzGxjOw/PiH8PyfWY2dni5V1Nho1r0f/Ll9w61oAS+b8yrIN\ni4mPjyckMJRxXyTt1U5Er9OzccJyPl45Go1Ww4l1foRdD6TpkPcJPH+bS7tO0Wr0h1jntKH7oi8A\niAmKYnm/2Xi2qknRaqWxzWuH9/uGiVhrhy8h5FLqsytfInV69oxfwXv/G4lGq+HC2n1EXQui1tD3\nCDt/m5u+p6k3titWOW1os3gwAA+Do/i771yubT1OwVrl6LnTMM/stt85bu06Y9E9lzo9V0Yvo/Ka\nMQithqDVe3l0NZBiIzsR63+LiB2nKNi3GfnqlkcfryP+wSMuDF4MgHV+e6qsGY3US56FRnN+4EKL\ndCa9339O+JXPVo5Bo9VwdJ0fodcDaTmkE3fP3+LCrlO0G90d65w29F40BID7QZH81G9WhnWlx4iJ\nMzhx5hwxMbE0bt+dz/p+xHttmr12vHqdnuUTfmL0yolotFr81u0i8Po93h/aldvnbnBq1wmKehZn\n6NKvsLW3o3ITbzoN6coIn8EZ0qPT6Zg15jvmr56DVqth05qt3LoWQP8Rfbnsf4X9Ow+xcfVWJs8f\nx4bDq4mNiWXMgEkAeFX3pOfAbsS/iEdKyYzRc3lgXK42a+z3TFk4ASsrK4LuBvP1l9NSTYPU6dk3\nfgVtfzPU40tr9xF9LYjqw94j/Nxtbvuepo6xHrdYkliPt/aZi8YqG+/9ZXien8c9YedgQ+/Im0Bm\ngZarsHRMSZF1EULogJefkAIYI6XcKoTwALZIKcsnke8FeEspBwohJgFxUsrZaenwdK751itScxuP\nt60SACe99p3orfDs7X+Hb8nxbt4P/7XzXK8/j0pfKJPpma3wW9f5kkH3fnst87ioYHeLK+Znr6nr\nVVEt1/8AUsoUrYGUMgAon4L7cmC58e9Jby5lCoVCkXHUDk0KhUKhUGQyWaG/VRlXhUKhUGQpssJs\nYWVcFQqFQpGlyAqLuZRxVSgUCkWWQnULKxQKhUKRyahuYYVCoVAoMhk1W1jxnyH6+cO3rvNO9ifp\nC70B9sZbtvVdZnMjh3P6Qv9PeFfrTVeemvtO9FavkMZ+2W+Ic9pn6Qv9S1HdwgqFQqFQZDLxWcC8\nKuOqUCgUiizFv9+0KuOqUCgUiiyGGnNVKBQKhSKTUbOFFQqFQqHIZPRZoGNYGVeFQqFQZCn+/aZV\nGVeFQqFQZDGywmxhzbtOgOL/H5Onj+bgyW34HlhPec8yKcpUqFiWXQfXc/DkNiZPH53gPnzMQHwP\nrGfHvj/5/a+lODk7moWrWKk8dyL8adXWJ1X9FetX4rs9C/lh32LafdoxmX+ZamWZsXUOq27+RfWW\nNZP557DLweJjv9B7cr9081qzQTX+PPAb6w+toufAbsn8raytmLZkEusPrWLZliW4uJuvVXVyK8C+\n69vpPqBLgtv4uaPYcW4ja/YsT1c/QNn6FZm0+3u+9ptH00/bJfMvXq0Mo7fMYMGN1VRqUd3Mr8NX\n3Ri/cw4Tds2l88TeFul7XZ3tv+rG+B2zGb9jNlVaJ7//aVGxfiXm7FnId/sW0zaFsi1drSzTts7h\nt5t/US2Vsl147Bd6WVC2ljJu2lzqtepC++4DMiW+Wg2rs/7AKjYeXkOvgd2T+VtZWzFjyddsPLyG\nFVuXJtSpcl5lWO27jNW+y1izazkNW9QzC6fRaFi181d+WDkzTf3l6nsxZfcPfOM3n+aftk/mX6Ja\nGcZtmcmSG2uo3KJGgnupmuWYsG1Wwm/R1d/xalr1VW5BusgM/CxBCNFcCHFVCHFDCPFVCv5DhRCX\nhBDnhBC7hRDpHoabrnEVQuiEEGeFEBeFEP5GJRqjn7cQYl464XsJIRakpydJmDEZkU8SdrkQ4rYx\nzaeFEBl6eoUQccb/XYUQf75qOjKgb5IQIsiY3rNCiBmZHH97IURZk+vJQogmmanDlEZN6lKkWCHq\neLdk1JBJTJ8zPkW56bPHM3LI19TxbkmRYoVo2KQOAEvmL8Onbkea1X+f3Tv28eWITxPCaDQaxkwc\nwr49h1LVLzQa+kzpz/SekxnaZBC129bFrYS7mUxkcCSLhs3j0Mb9KcbRediHXDp2Md28ajQaRk4b\nwhfdRtC5QQ+atmtMkRLmz1y7rq2IjXlIx9ofsuqndQwaZ/4CHjppEIf3HDNz27J2O4O7jUhXP4DQ\nCLpM7suCXtOY7DOEqm1r41zczUwmOjiSlcMXcWLjQTP3opVLUsy7FFObD2dK02EUrliMEjXKkh6v\no7N8w0oUKleEb1qOZGb7sfh80hYbuxwW5lVD7yn9mdlzMsObDKJWKmW7JI2y7TTsQy5bULYZoX1L\nH5bMnZopcWk0GkZNG8qgbsN5r353mrdvQpGSHub6urYm9sFD2tXqwu9L1/LFOMMzcvPqLbo3/5iu\nPr0Z+OEwxn47Aq028Sjnrv06cfv6nTT1C42GDyf35Yde3zDBZwjV2tbGpbj5PY4OjmTZ8IUcT1K2\nV49cZHLLEUxuOYLZXb/m+ZPnXNrv/xp3I3X0GfilhxBCCywEWgBlga6m70wjZwBvKaUn8CfwbXrx\nWtJyfSKl9JJSlgN8gJbARAAp5Ukp5WAL4sgor2xcjYyQUnoBXwE/vkoEUspgKeX7GQljLKRX4Tvj\nPfaSUib7anpN2mOoMABIKSdIKXdlso4EmrZsyJ9rNgFw+uQ5cufORQGn/GYyBZzyY5fLltMnDA/e\nn2s20axlIwDiHj5KkMuRMwdSJn579v7kQ7Zt9iUyIvUdkop7lSAsIITwe2HoXsRzePNBqvqYt5wi\nAsO5e+UOen3y79oi5YuRJ38ezu0/m25ey1Uqw72AIILuhhD/Ih7fjbup36yOmUy9ZnXY+sd2APZs\n2UfVOpUT/Oo3r0PQ3WBuXQswC3PmmD+x92PT1Q/g4VWciDuhRN4LR/dCx8nNh6mYpLUQHRhB0JW7\nZvcSQCKxym5NNqtsZLO2QptNy8OIB29Up0sJd64fu4Rep+f5k2cEXr5D2fpeFuW1uFcJQk3K9sjm\ng3gnKdtIY9nKVMrW3sKyzQjeXhWwz50rU+IqX6kMgQGBBN0NJv5FPDs27qJBkjrVoHkdtqz7B4Dd\nW/yoWrcKAE+fPEOn0wFgnd3a7N4XcHGkbuOa/L1qc5r6i5iVbTwnNh/Cq6m3mUxUKmVrSpWWNbjg\nd4bnT59bnvkMoEda/LOAasANKeUtKeVzYA1g1h0jpdwrpXxsvDwKuJMOGeoWllKGA58AA4WBBkKI\nLQBCiGpCiMNCiDPG/0uZBC0ohNhubHZPfOkohOguhDhubLH9KITQGltuOYxuv6chpzW2Ui8IIc4L\nIYakkOT9QHFjHMWMaTglhDgghChtdC8ihDgihDghhJhikjYPIcQF4985hRDrjF0Ca4UQx4QQ3ka/\nOGNr8BhQUwhRRQixz6hnhxDCJS39qSGECBBC5Df+7S2E8DP+PUkI8asQwk8IcUsIMdgkTA9jGv2F\nEP8TQtQC2gKzjPeumPGevW+Ub2wsr/PGOLOb6P7a2PI/n15aTXF2cSI4KDThOiQ4DGcXp2QyIcFh\nqcqMHDuY4+d30aFTK2ZPX2AMU4AWrRrzv2Xr0tTv4OxAVEhkwnVUSBR5nR0sSrsQgo/G9ea3aSss\nknd0zk9YcHjCdVhIBI4u5t3YBUxkdDodcbGPsHewxyaHDT0++5Cf5iy3SFdq5HFy4H5wVML1/ZAo\n8jhZlt/bp69z9chFZpxYyszjS7m035/Qm0FvVGfg5TuUa+CFlY01tnlzUapmOfK65LMobN7XLNvu\n43rzu4Vl+65wdHYkNCixToWHRFAgydCIo7MjoUnqVB4HewDKVyrLH37/Y93eFUwbNTvB2A6fPJgf\npi5O8YPSlDxODkSblW00eZwsKx9TqrWpzfFNB9MXfEUyuVvYDbhnch1odEuNvsA/6UWa4TFXKeUt\nY7gCSbyuAPWklJWACcA0E79qQDfAC+hkNBZlgA+A2sZWpg7oZmy5vWwtd0tNzhiXm5SyvJSyArAs\nheS2Ac4b/14KDJJSVgGGA4uM7j8Ai6WUVYHQ5FEA8Blw39glMAWoYuJnC1yQUlYHjgHzgfeNen4F\nvklHP8AQk27hZqmkwZTSQDMM93WiEMJKCFEOGAs0klJWBL6QUh4GNmFsyUspb76MQAhhAywHPjDe\nv2zApyY6IqWUlYHFxvRahBDJF6Al/cJNT+bbb+ZRrUITNvyxld79PgRg0rRRTPv6O/T6tDt6BCks\ngLPwCWvaowVn954ye4GnqesV84qU9B/Rh9U//cGTx6+3P7IlaUgNx8JOOBd3Y0yNAYyu0Z9StcpT\nvFrKY+SZpfPygXNc2HuGEeun0nfeF9w6fQ290QCkq/c1ytbHWLbRFpbtu+J1n58LZy7RqcFHfNSi\nH70Hdcc6uzV1m9QiOjKGy+euWqA/BUcLy/Yl9o55cCtViItvqEsYMtYtLIT4RAhx0uT3SZLoUsx1\nSnqFEN0Bb2BWeml81dnCKSXGHlghhChhTJiViZ+vlDLKmLj1QB0M591WAU4YK0sOIJzkNE5FbjNQ\nVAgxH9gK7DQJM0sIMQ6IAPoKIeyAWsAfJhUzu/H/2sB7xr//B6Q02l8HgxFGSnlBCHHOxE8H/GX8\nuxRQHvA16tECIenoB0O38OwU9KbGVinlM+CZECIccAIaAX9KKSON6Uxvd/lSwG0p5TXj9Qrgc+B7\n4/V64/+ngOQzRzBUWuCTr776yvEfv7VohBb/MxdwdUuctOPi6kRYqHmxhgSH4uLqlKYMwN9/bmXF\n2kXMmbEQT69yLPzZUJ8dHPLSyKcuy8Ys5eRO8/HKqNAo8rkkdkPnc8nH/TDLNtovWbkUpauWxeej\nFtjY2pDNKhtPHz1l9cz/pSgfHhKBk2viN6aTiyORoeYv7zCjTHhIBFqtFrvctjy4H0u5SmVo1Ko+\ng8YNIFduO/R6ybNnz/lj2fqkatLkfmgUeV0TWxZ5XfLxIPy+RWG9mlXj9pnrPHts2MD9ot8ZilQq\nwY3jl9+YToDtCzewfeEGAPr8MJjw26l905oT/RplWyJJ2WqNZbsmlbJ9V4SHhOPsllinCrg4EhEW\nmVwmhTplyu3rd3jy+CnFShehYrUK1G9amzqNa2Cd3RrbXLZMXTCeDcN+Tqb/fmg0DmZl60BMeMYO\nqvBuXYszO46ji7fso+lV0GVgtrCUcimGxk1qBAIFTa7dgeCkQsIwV2UsUN/4/k2TDBtXIURRDAYl\nHDD9zJ0C7JVSdhBCeAB+Jn5J74TEYKBXSClHkzapygkhKmJowX0OdAb6GL1GSCn/NJHLDcQYW74p\nkV5JpbUfyFMppc5E7qKU0mwSlQX6UyKexJ4FmyR+pgWrw1COgowt/0pvj5OXOl7GnwzTSuvuUF4C\nNPKpR+9+Xdm4/h8qe3vyMDaO8KQvh7BI4uIeU9nbk9Mnz/F+l7YsW7oKgCJFC3H71l0AmrZoyM3r\ntwGoVal5Qvi5C6aye+c+YvbeTpamm/7XcS7igmPBAkSHRlOrTR3mDbbspJP5X3yX8Hf99xtR1LNY\nqoYV4NLZKxQq4o5rQRfCQyPwadeY8Z9PNpM5sPMQrTo15/ypizRqXZ8TB08D8EmHQQky/Yb15smj\nJxk2rAB3/G9SwMOFfO6OxIRF492mFr8OTnOOYQLRwZHU6dKYHYs0IAQlqpdlz6/b3qhOoRHkzG3L\no5g43EoXwq10IS4fsKyFk7Rsa7apwwILy3ahSdnWM5btv82wAlw8e4WCRQom1Klm7Zow5rOvzWT2\n7ThE684tOHfqIo1bN0ioU64FXQgLDken0+Hi7oRHsUKE3AtlwbQfWTDNMPWkSs1K9Pi0C+MGTqFK\ndpdk+gP8b1DAw4X87gW4HxZN1Ta1+XnwDxnKQ7W2tVn/7apXvAOWkcmbSJwASgghigBBQBfgQ1MB\nIUQlDPN3mhuHR9MlQ8ZVCOEILAEWSCllku4Je2PCAHolCeojhHAAnmCYYNMHeAxsFEJ8J6UMN/rn\nklLeAV4IIayklC+A3SnJAY+A51LKv4QQNzF0caaIlDJWGGYQd5JS/iEMCfeUUvoDhzDczN8wdDen\nxEEMxnuvMMwiq5CK3FXAUQhRU0p5RAhhBZSUUl5MQ39qBGBosf9DYss6LXYDG4z3KUoI4WBsvT7E\ncL+ScgXwEEIUl1LeAD4C9lmgJ032+O6nkU9dDp76h6dPnjB0YOJs4R37/qRZfcMcsTHDpzB34VRs\nbGzw23WAPbsOADB64hCKFvdA6iWB94IZPWxyinpSQ6/T8+uEnxizciIarRa/dbsIvH6PTkO7cuvc\nDU7tOkExz+IMW/oVtvZ2VGniTachXRnuk/F5eTqdjm/Hfs+8VbPRajVsWrONW9cC6D+iD5f9r7J/\n5yE2rt7K1/PGsv7QKmJjHjL200npxjt10QSq1KxEHgd7tpz8k6VzlrFp9dZU87tmwq8MWjkWjVbD\n4XV7CbkeSOshnbl7/ibndp2isGcx+v84nJz2tlRoXIXWQzozpekwTm87Sqla5Rm3YzZIuLjvLOd3\nn0o3fa+jU2uVjWF/GMr0adxjlg2Zj15n2U6xep2e5RN+YnSSsn1/aFduG8u2qGdxhhrLtrKxbEe8\nQtlmhBETZ3DizDliYmJp3L47n/X9iPfaWDK6kxydTsfMMXNZuHouGq2GTWu2cuvabQaM6Msl/yvs\n33mIv1dvYcr88Ww8vIYHMbGMHjAJgErVPek1sDvxL+LRSz3TR88hJjr9CWqm6HV6Vk34hS9XjkVo\nNRxat5fg64G0HfIBd87fxH/XSTw8i/HZjyPIaW+LZ+MqtBvSmYlNDUcE5nN3JK9Lfq4dvfRK+beU\nzDStUsp4IcRAYAeG3sZfje/sycBJKeUmDN3AdiT2Pt6VUrZNK16R3liJEEKHYdzSCkNr6n/AXCml\nXgjRABgupWwtDEteVmDoit0DfCSl9BBC9MIww9gWw+SiVVLKr41xfwCMxtBCewF8LqU8KoSYiWEi\nzmnjuGsyOQyGehmJrbvRUsp/hBDLgS2mLVejriIYxg9djHlZI6WcbHRfheFD4y9gnJTSztj63iKl\nLC+EsDXmrSSGKdnlgS5SyutCiDgppZ2JHi9gHoaPjWzA91LKn9LQPwmIS9otLISoC/wChGEYy/WW\nUjZIKi8Mk65aSykDhBA9gREYWptnpJS9hBC1gZ8wtETfB8a/vD9CiMbAbGM6TwCfSimfCSECjPoi\nhWHi1mwpZQPS4GXL9W1SO1fxt60SgFsv3s15rt7W/53zXGPkm5llmh7/pfNcU2q5vi1+CvjjtXYH\n7u/RyeL3zY+vqetVSde4KhKW2FhJKZ8KIYphaCWWNE7bVqCM69tAGdc3jzKub4fXNa79MmBcX1fX\nq6K2P7SMnBi6hK0wjFV+qgyrQqFQvBsyMqHpXaGMqwVIKR9imH6tUCgUineMVMZVoVAoFIrMRR2W\nrlAoFApFJqPPAnOFlHFVKBQKRZbi329alXFVKBQKRRYjkzeReCMo46rIFE6UdUxfKJNZfs8ufaE3\nQClr23eid1qw31vX6ZjT/q3rBHDLkfHN4jODd7EkBuDY+ZVvXefX3uPeus7MQs0WVigUCoU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/fTvEXnk+LMmPYTXbq1Z96S7zjy11Hu/eejqcem//wZ3S66CoBH7hvFS6+PoUjRwsyc/jMzp/90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\nH/J1BYOnywqPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11099b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征之间的相关性\n",
    "correlation = data.corr()\n",
    "sns.heatmap(correlation, annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kPldTtvWacb5CY42guqqqXjjDosOGGogkSdI8WRMnSZIkSWPEJE6SJEmSxojN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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a608d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist(bins=50, figsize=(15, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程\n",
    "将缺失值替换为中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 计算各特征的中位数，并将0替换为中位数\n",
    "data['BMI'] = data['BMI'].replace(0, data['BMI'].median())\n",
    "data['Pregnancies'] = data['Pregnancies'].replace(0, data['Pregnancies'].median())\n",
    "data['Glucose'] = data['Glucose'].replace(0, data['Glucose'].median())\n",
    "data['BloodPressure'] = data['BloodPressure'].replace(0, data['BloodPressure'].median())\n",
    "data['SkinThickness'] = data['SkinThickness'].replace(0, data['SkinThickness'].median())\n",
    "data['Insulin'] = data['Insulin'].replace(0, data['Insulin'].median())\n",
    "data['DiabetesPedigreeFunction'] = data['DiabetesPedigreeFunction'].replace(0, data['DiabetesPedigreeFunction'].median())\n",
    "data['Age'] = data['Age'].replace(0, data['Age'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = data['Outcome']\n",
    "X = data.drop('Outcome', axis = 1)\n",
    "\n",
    "# 分割训练数据和测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 21, test_size = 0.2)\n",
    "X.head();\n",
    "\n",
    "#数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_X = StandardScaler()\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogisticRegressionCV: L2\n",
    "class sklearn.linear_model.LogisticRegressionCV(Cs=10, fit_intercept=True, cv=’warn’, dual=False, penalty=’l2’, scoring=None, solver=’lbfgs’, tol=0.0001, max_iter=100, class_weight=None, n_jobs=None, verbose=0, refit=True, intercept_scaling=1.0, multi_class=’warn’, random_state=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[ 0.77419355,  0.76612903,  0.76612903,  0.76612903,  0.76612903,\n",
       "          0.76612903],\n",
       "        [ 0.77235772,  0.77235772,  0.77235772,  0.77235772,  0.77235772,\n",
       "          0.77235772],\n",
       "        [ 0.82113821,  0.82113821,  0.82113821,  0.82113821,  0.82113821,\n",
       "          0.82113821],\n",
       "        [ 0.79508197,  0.79508197,  0.79508197,  0.79508197,  0.79508197,\n",
       "          0.79508197],\n",
       "        [ 0.75409836,  0.75409836,  0.75409836,  0.75409836,  0.75409836,\n",
       "          0.75409836]])}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用LogisticRegressionCV寻找最佳正则参数\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "#使用缺省正则函数　penalty = l2\n",
    "Cs = [ 0.1, 0.65, 0.7, 1, 10, 100]\n",
    "lrcv_l2 = LogisticRegressionCV(Cs = Cs, cv = 5, #scoring = 'neg_log_loss', #multi_class='multinomial'\n",
    "                               penalty = 'l2').fit(X_train, y_train)\n",
    "\n",
    "lrcv_l2.scores_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "只有两类所以只计算一类的scores就够了？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.1])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最佳正则参数\n",
    "lrcv_l2.C_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegressionCV_L1 测试集评价结果： 0.753246753247\n"
     ]
    }
   ],
   "source": [
    "# 测试集上的评价结果\n",
    "print('LogisticRegressionCV_L1 测试集评价结果：', lrcv_l2.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义results存放scores, accuracy存放测试集准确率\n",
    "results = []\n",
    "accuracy = []\n",
    "results.append(lrcv_l2.scores_[1][:, Cs.index(lrcv_l2.C_[0])])\n",
    "accuracy.append(lrcv_l2.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预测结果不一样，但是测试集上的准确率有可能是一样的？？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LogisticRegressionCV: L1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.65])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# penalty = l1\n",
    "Cs = [0.1, 0.65, 0.7, 1, 10, 100]\n",
    "lrcv_l1 = LogisticRegressionCV(Cs = Cs, cv=5, penalty = 'l1', #scoring = 'neg_log_loss',\n",
    "                              solver='liblinear', multi_class = 'ovr').fit(X_train, y_train)\n",
    "# 最佳正则参数\n",
    "lrcv_l1.C_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[ 0.75806452,  0.76612903,  0.76612903,  0.76612903,  0.76612903,\n",
       "          0.76612903],\n",
       "        [ 0.76422764,  0.77235772,  0.77235772,  0.77235772,  0.77235772,\n",
       "          0.77235772],\n",
       "        [ 0.82113821,  0.82926829,  0.82926829,  0.82926829,  0.82113821,\n",
       "          0.82113821],\n",
       "        [ 0.79508197,  0.79508197,  0.79508197,  0.79508197,  0.79508197,\n",
       "          0.79508197],\n",
       "        [ 0.75409836,  0.75409836,  0.75409836,  0.75409836,  0.75409836,\n",
       "          0.75409836]])}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练集上交叉验证的评价结果\n",
    "lrcv_l1.scores_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegressionCV_L1 测试集评价结果： 0.75974025974\n"
     ]
    }
   ],
   "source": [
    "# 测试集上的评价结果\n",
    "print('LogisticRegressionCV_L1 测试集评价结果：', lrcv_l1.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results.append(lrcv_l1.scores_[1][:, Cs.index(lrcv_l1.C_[0])])\n",
    "accuracy.append(lrcv_l1.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridSearchCV\n",
    "class sklearn.model_selection.GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=None, iid=’warn’, refit=True, cv=’warn’, verbose=0, pre_dispatch=‘2*n_jobs’, error_score=’raise-deprecating’, return_train_score=’warn’)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV_lr 训练集最佳评价结果： 0.456783340319\n",
      "最佳正则参数和正则函数： {'C': 0.65, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "# 用GridSearchCV寻找最佳正则函数，正则参数\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "penaltys = ['l1', 'l2']\n",
    "Cs = [0.1, 0.65, 0.7, 1, 10, 100]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_grid = LogisticRegression()\n",
    "grid = GridSearchCV(lr_grid, tuned_parameters, cv = 5, scoring = 'neg_log_loss')\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "print('GridSearchCV_lr 训练集最佳评价结果：', -grid.best_score_)\n",
    "print('最佳正则参数和正则函数：', grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75974025974025972"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 最佳参数下的测试集的评价结果\n",
    "grid.best_estimator_.score(X_test, y_test)\n",
    "#pd.DataFrame(grid.cv_results_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GridSearchCV的结果也是l2更好，但是为什么最佳正则参数C不一样？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### LinearSVC\n",
    "class sklearn.svm.LinearSVC(penalty=’l2’, loss=’squared_hinge’, dual=True, tol=0.0001, C=1.0, multi_class=’ovr’, fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC_L2 训练集上最佳评价结果： 0.78664495114\n",
      "最佳正则参数： {'C': 10}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# 缺省正则函数，penalty = l2\n",
    "linear_SVC_l2 = LinearSVC()\n",
    "# 用GridSearchCV寻找最佳正则参数\n",
    "grid_linear_SVC_l2 = GridSearchCV(linear_SVC_l2, param_grid = dict(C=[0.01, 0.1, 1, 10, 100]), cv = 5)\n",
    "grid_linear_SVC_l2.fit(X_train, y_train)\n",
    "\n",
    "print('LinearSVC_L2 训练集上最佳评价结果：', grid_linear_SVC_l2.best_score_)\n",
    "print('最佳正则参数：', grid_linear_SVC_l2.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC_L2 测试集上的评价结果： 0.75974025974\n"
     ]
    }
   ],
   "source": [
    "# 显示在测试集上的评价结果\n",
    "print('LinearSVC_L2 测试集上的评价结果：', grid_linear_SVC_l2.best_estimator_.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.00124021,  0.00323739,  0.01596932,  0.01546173,  0.01605134]),\n",
       " 'mean_score_time': array([ 0.00032687,  0.0002512 ,  0.0002193 ,  0.00020847,  0.00019779]),\n",
       " 'mean_test_score': array([ 0.78175896,  0.78338762,  0.78338762,  0.78664495,  0.752443  ]),\n",
       " 'mean_train_score': array([ 0.78420561,  0.78542346,  0.78623729,  0.78745763,  0.74835118]),\n",
       " 'param_C': masked_array(data = [0.01 0.1 1 10 100],\n",
       "              mask = [False False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'C': 0.01}, {'C': 0.1}, {'C': 1}, {'C': 10}, {'C': 100}],\n",
       " 'rank_test_score': array([4, 2, 2, 1, 5], dtype=int32),\n",
       " 'split0_test_score': array([ 0.76612903,  0.76612903,  0.76612903,  0.76612903,  0.72580645]),\n",
       " 'split0_train_score': array([ 0.79387755,  0.7877551 ,  0.7877551 ,  0.7877551 ,  0.72653061]),\n",
       " 'split1_test_score': array([ 0.77235772,  0.77235772,  0.77235772,  0.7804878 ,  0.70731707]),\n",
       " 'split1_train_score': array([ 0.78615071,  0.79022403,  0.79022403,  0.79429735,  0.72708758]),\n",
       " 'split2_test_score': array([ 0.82113821,  0.82926829,  0.82926829,  0.83739837,  0.7804878 ]),\n",
       " 'split2_train_score': array([ 0.77189409,  0.77596741,  0.77800407,  0.77596741,  0.76374745]),\n",
       " 'split3_test_score': array([ 0.79508197,  0.79508197,  0.79508197,  0.80327869,  0.83606557]),\n",
       " 'split3_train_score': array([ 0.77642276,  0.77845528,  0.77845528,  0.7804878 ,  0.7703252 ]),\n",
       " 'split4_test_score': array([ 0.75409836,  0.75409836,  0.75409836,  0.74590164,  0.71311475]),\n",
       " 'split4_train_score': array([ 0.79268293,  0.79471545,  0.79674797,  0.79878049,  0.75406504]),\n",
       " 'std_fit_time': array([ 0.00039087,  0.00095696,  0.00108303,  0.00018301,  0.0002074 ]),\n",
       " 'std_score_time': array([  1.30696707e-04,   7.96460236e-05,   2.44005546e-05,\n",
       "          9.71344328e-06,   1.58579723e-06]),\n",
       " 'std_test_score': array([ 0.02376944,  0.02653009,  0.02653009,  0.0315188 ,  0.04904633]),\n",
       " 'std_train_score': array([ 0.00873317,  0.00711049,  0.00716963,  0.00843423,  0.01833483])}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_linear_SVC_l2.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "linear_SVC_l2_scores = np.array([grid_linear_SVC_l2.cv_results_[key] for key in ['split0_test_score', 'split1_test_score', \n",
    "                                                        'split2_test_score', 'split3_test_score', \n",
    "                                                        'split4_test_score']])[:,[grid_linear_SVC_l2.cv_results_['rank_test_score']==1][0]][:,0]\n",
    "results.append(linear_SVC_l2_scores)\n",
    "accuracy.append(grid_linear_SVC_l2.best_estimator_.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC_L1 训练集上最佳评价结果： 0.78338762215\n",
      "最佳正则参数： {'C': 1}\n"
     ]
    }
   ],
   "source": [
    "# penalty = l1\n",
    "linear_SVC_l1 = LinearSVC(penalty = 'l1', dual = False)\n",
    "# 用GridSearchCV寻找最佳正则参数\n",
    "grid_linear_SVC_l1 = GridSearchCV(linear_SVC_l1, param_grid = dict(C=[0.01, 0.1, 1, 10, 100]), cv = 5)\n",
    "grid_linear_SVC_l1.fit(X_train, y_train)\n",
    "\n",
    "print('LinearSVC_L1 训练集上最佳评价结果：', grid_linear_SVC_l1.best_score_)\n",
    "print('最佳正则参数：', grid_linear_SVC_l1.best_params_)\n",
    "#grid_linear_SVC_l1.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC_L1 测试集上的评价结果： 0.75974025974\n"
     ]
    }
   ],
   "source": [
    "# 显示在测试集上的评价结果\n",
    "print('LinearSVC_L1 测试集上的评价结果：', grid_linear_SVC_l1.best_estimator_.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "linear_SVC_l1_scores = np.array([grid_linear_SVC_l1.cv_results_[key] for key in ['split0_test_score', 'split1_test_score', \n",
    "                                                        'split2_test_score', 'split3_test_score', \n",
    "                                                        'split4_test_score']])[:,[grid_linear_SVC_l1.cv_results_['rank_test_score']==1][0]][:,0]\n",
    "results.append(linear_SVC_l1_scores)\n",
    "accuracy.append(grid_linear_SVC_l1.best_estimator_.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RBF 核的 SVM\n",
    "svc = SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
    "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
    "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
    "    tol=0.001, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'C': array([  1.00000e-03,   1.00000e+00,   1.00000e+03]), 'gamma': array([  1.00000e-03,   1.00000e+00,   1.00000e+03])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# 需要调优的参数\n",
    "Cs_svc = np.logspace(-3, 3, 3)\n",
    "gamma_svc = np.logspace(-3, 3, 3)\n",
    "\n",
    "svc_rbf = SVC(kernel = 'rbf')\n",
    "grid_SVC_rbf = GridSearchCV(svc_rbf, param_grid = dict(C = Cs_svc, gamma = gamma_svc), cv = 5)\n",
    "grid_SVC_rbf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 0.0053504 ,  0.00512366,  0.00442295,  0.00479484,  0.00792794,\n",
       "         0.00655017,  0.00842438,  0.00941563,  0.00906401]),\n",
       " 'mean_score_time': array([ 0.00131793,  0.00128469,  0.00110602,  0.00109701,  0.0014658 ,\n",
       "         0.00117583,  0.00093527,  0.00145254,  0.00123539]),\n",
       " 'mean_test_score': array([ 0.66123779,  0.66123779,  0.66123779,  0.66123779,  0.67915309,\n",
       "         0.66123779,  0.7752443 ,  0.67915309,  0.66123779]),\n",
       " 'mean_train_score': array([ 0.66123832,  0.66123832,  0.66123832,  0.66286434,  0.97068281,\n",
       "         1.        ,  0.80048981,  1.        ,  1.        ]),\n",
       " 'param_C': masked_array(data = [0.001 0.001 0.001 1.0 1.0 1.0 1000.0 1000.0 1000.0],\n",
       "              mask = [False False False False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'param_gamma': masked_array(data = [0.001 1.0 1000.0 0.001 1.0 1000.0 0.001 1.0 1000.0],\n",
       "              mask = [False False False False False False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'C': 0.001, 'gamma': 0.001},\n",
       "  {'C': 0.001, 'gamma': 1.0},\n",
       "  {'C': 0.001, 'gamma': 1000.0},\n",
       "  {'C': 1.0, 'gamma': 0.001},\n",
       "  {'C': 1.0, 'gamma': 1.0},\n",
       "  {'C': 1.0, 'gamma': 1000.0},\n",
       "  {'C': 1000.0, 'gamma': 0.001},\n",
       "  {'C': 1000.0, 'gamma': 1.0},\n",
       "  {'C': 1000.0, 'gamma': 1000.0}],\n",
       " 'rank_test_score': array([4, 4, 4, 4, 2, 4, 1, 2, 4], dtype=int32),\n",
       " 'split0_test_score': array([ 0.66129032,  0.66129032,  0.66129032,  0.66935484,  0.67741935,\n",
       "         0.66129032,  0.75      ,  0.66935484,  0.66129032]),\n",
       " 'split0_train_score': array([ 0.66122449,  0.66122449,  0.66122449,  0.65918367,  0.96734694,\n",
       "         1.        ,  0.80816327,  1.        ,  1.        ]),\n",
       " 'split1_test_score': array([ 0.65853659,  0.65853659,  0.65853659,  0.65853659,  0.69918699,\n",
       "         0.65853659,  0.76422764,  0.69918699,  0.65853659]),\n",
       " 'split1_train_score': array([ 0.66191446,  0.66191446,  0.66191446,  0.66395112,  0.97148676,\n",
       "         1.        ,  0.79633401,  1.        ,  1.        ]),\n",
       " 'split2_test_score': array([ 0.65853659,  0.65853659,  0.65853659,  0.6504065 ,  0.71544715,\n",
       "         0.65853659,  0.82926829,  0.69105691,  0.65853659]),\n",
       " 'split2_train_score': array([ 0.66191446,  0.66191446,  0.66191446,  0.66395112,  0.97352342,\n",
       "         1.        ,  0.79226069,  1.        ,  1.        ]),\n",
       " 'split3_test_score': array([ 0.66393443,  0.66393443,  0.66393443,  0.66393443,  0.6557377 ,\n",
       "         0.66393443,  0.79508197,  0.63934426,  0.66393443]),\n",
       " 'split3_train_score': array([ 0.66056911,  0.66056911,  0.66056911,  0.66056911,  0.97154472,\n",
       "         1.        ,  0.78658537,  1.        ,  1.        ]),\n",
       " 'split4_test_score': array([ 0.66393443,  0.66393443,  0.66393443,  0.66393443,  0.64754098,\n",
       "         0.66393443,  0.73770492,  0.69672131,  0.66393443]),\n",
       " 'split4_train_score': array([ 0.66056911,  0.66056911,  0.66056911,  0.66666667,  0.9695122 ,\n",
       "         1.        ,  0.81910569,  1.        ,  1.        ]),\n",
       " 'std_fit_time': array([ 0.00041981,  0.00059748,  0.00024198,  0.00032608,  0.0001652 ,\n",
       "         0.00057674,  0.00040835,  0.00027693,  0.0004328 ]),\n",
       " 'std_score_time': array([  1.85948175e-04,   1.26340126e-04,   9.67804418e-05,\n",
       "          9.69469164e-05,   1.87018913e-04,   8.24883355e-05,\n",
       "          1.35561911e-04,   9.51580610e-05,   1.41642116e-04]),\n",
       " 'std_test_score': array([ 0.00241116,  0.00241116,  0.00241116,  0.00641554,  0.02554202,\n",
       "         0.00241116,  0.0331041 ,  0.02245857,  0.00241116]),\n",
       " 'std_train_score': array([ 0.0006017 ,  0.0006017 ,  0.0006017 ,  0.00266965,  0.00209555,\n",
       "         0.        ,  0.01169406,  0.        ,  0.        ])}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_SVC_rbf.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC_RBF 训练集上最佳评价结果： 0.775244299674\n",
      "最佳正则参数： {'C': 1000.0, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "print('SVC_RBF 训练集上最佳评价结果：', grid_SVC_rbf.best_score_)\n",
    "print('最佳正则参数：', grid_SVC_rbf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC_RBF 测试集评价结果: 0.746753246753\n"
     ]
    }
   ],
   "source": [
    "# RBF核的SVM在测试集上的评价结果\n",
    "print('SVC_RBF 测试集评价结果:', SVC(C = 1000, gamma = 0.001, kernel = 'rbf').fit(X_train, y_train).score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SVC_RBF_scores = np.array([grid_SVC_rbf.cv_results_[key] for key in ['split0_test_score', 'split1_test_score', \n",
    "                                                        'split2_test_score', 'split3_test_score', \n",
    "                                                        'split4_test_score']])[:,[grid_SVC_rbf.cv_results_['rank_test_score']==1][0]][:,0]\n",
    "results.append(SVC_RBF_scores)\n",
    "accuracy.append(SVC(C = 1000, gamma = 0.001, kernel = 'rbf').fit(X_train, y_train).score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.75324675324675328,\n",
       " 0.75974025974025972,\n",
       " 0.75974025974025972,\n",
       " 0.75974025974025972,\n",
       " 0.74675324675324672]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JndeZL6yVp3xksUNYEDe/4Rkzms/6mZz1o8UwzjV0kiRpS+NcY06SA4EVwGUD\n865Isns7/FSayxMkSZoWEzpJkmZgzGvMobkZynlVVZ157wdeCXwiyRdoum++c+GilyQtF3a5lCRp\nhqa6xrwdXjNi3o8Dj5234CRJWwXP0EmSJElST5nQSZIkSVJPmdBJkiRJUk8t62vodt11VzZu3Dhn\ny0uGPXJoZlasWMGdd945Z8ubK0u1zpZqfWl63L+mx/qSJElTWdYJ3caNG+ncVGxJmcvkcC4t1Tpb\nqvWl6XH/mh7rS5IkTcUul5IkSZLUUyZ0kiRJktRTJnSSJEmS1FMmdJIkSZLUUyZ0kiRJktRTJnSS\nJEmS1FMmdJIkSZLUUyZ0kiRJktRTy/rB4pIkaXMrT/nIYoewIG5+wzMWOwRJWhCeoZMkSZKknjKh\nkyRJkqSeMqGTJEmSpJ4yoZMkSZKknjKhkyRJkqSeMqGTJEmSpJ4yoZMkSZKknjKhkyRJkqSeMqGT\nJEmSpJ4yoZMkSZKknjKhkyRJkqSeMqGTJEmSpJ4yoZMkSZKknjKhkyRJkqSeMqGTJEmSpJ4yoZMk\nSZKknjKhkyRJkqSe2m6xA5AkSZK0dVh5ykcWO4R5d/MbnrGg6/MMnSRJkiT1lAmdJEmSJPWUCZ0k\nSZIk9ZQJnSRJkiT1lAmdJEmSJPXUWAldkqOSXJ9kXZJThkw/M8lV7d8NSe7qTHtTkmuSXJfkLUky\nlxsgSZIkSVurKR9bkGRb4G3AkcB64PIkF1bVtRNlquqkTvkTgce3r38OeArw2HbyvwGHA5fOUfyS\nJEmStNUa5wzdwcC6qrqxqu4FzgOOnaT8ccC57esCdgR2AB4EbA98Y+bhSpIkSZImjPNg8T2BWzvD\n64FDhhVMsi+wH3AJQFVdluSTwNeAAG+tqutmFbG0hNRrdoE1D1vsMLZQr9llsUOQJEnSAhgnoRt2\nzVuNKLsauKCq7gdIsj9wELBXO/3jSQ6rqk9vtoLkBOAEgH322WecuKUlIa/9DlWjPg6LJwm1ZrGj\nkCRJ0nwbp8vlemDvzvBewO0jyq5mU3dLgF8BPltVd1fV3cBHgScPzlRVZ1XVqqpatfvuu48XuSRJ\nkiRt5cZJ6C4HDkiyX5IdaJK2CwcLJTkQWAFc1hl9C3B4ku2SbE9zQxS7XEqSJEnSHJgyoauq+4BX\nABfTJGPnV9U1SU5Pckyn6HHAebV5/7MLgK8AXwCuBq6uqn+es+glSZIkaSs2zjV0VNVFwEUD404b\nGF4zZL77gd+aRXySJC1ZSY4C3gxsC7yrqt4wMP1M4BfawYcAP1pVD+9M34XmYOk/VtUrFiZqSdJy\nMlZCJ0mSNjeb57R2vA741AI4q0fCAAAP9UlEQVSEK0lapsa5hk6SJG1pNs9pJckTgR8DPjavUUqS\nljUTOkmSZmbYc1r3HFZw8DmtSbYB/gL4g3mOUZK0zJnQSZI0MzN+TivwMuCiqrp1RPlNK0lOSLI2\nydoNGzbMMFRJ0nLlNXSSJM3MdJ/T+vLO8M8ChyZ5GfBQYIckd1fVKYMzVtVZwFkAq1atGpUwSpK2\nUiZ0kiTNzAPPaQVuo0nanjtYaNhzWqvqeZ3pxwOrhiVzkiRNxS6XkiTNwCyf0ypJ0pzwDJ0kSTM0\n0+e0Dkw/Gzh7jkOTJG0lPEMnSZIkST21rM/Q1Wt2gTUPW+wwhqrX7LLYIUiSJEnquWWd0OW132Gp\nXrKQhMk74UiSJEnS5OxyKUmSJEk9ZUInSZIkST1lQidJkiRJPbWsr6GD5lq1pWjFihWLHcJQS/VG\nMt5EZnlw/5IkSZpbyzqhm8sboiRZsjdYmUtL9UYy3kRmeXD/kiRJmlt2uZQkSZKknjKhkyRJkqSe\nMqGTJEmSpJ4yoZMkSZKknjKhkyRJkqSeMqGTJEmSpJ4yoZMkSZKknjKhkyRJkqSeMqGTJEmSpJ4y\noZMkSZKknjKhkyRJkqSeMqGTJEmSpJ4yoZMkSZKkntpusQPQ0pNksUPYwooVKxY7hJGsr+mxviRJ\nkuaOCZ02U1Vztqwkc7q8pcj6mh7rS5IkaW7Z5VKSJEmSesqETpIkSZJ6yoROkiRJknrKa+gkaYmq\n1+wCax622GFsoV6zy2KHIEmSWiZ0krRE5bXfWZI3fklCrVnsKCRJEtjlUpIkSZJ6y4ROkiRJknrK\nhE6SJEmSesqETpIkSZJ6aqyELslRSa5Psi7JKUOmn5nkqvbvhiR3dabtk+RjSa5Lcm2SlXMXviRJ\nkiRtvaa8y2WSbYG3AUcC64HLk1xYVddOlKmqkzrlTwQe31nEe4AzqurjSR4K/HCugpckSZKkrdk4\nZ+gOBtZV1Y1VdS9wHnDsJOWPA84FSPIoYLuq+jhAVd1dVffMMmZJkiRJEuMldHsCt3aG17fjtpBk\nX2A/4JJ21COBu5J8MMmVSf6sPeMnSZIkSZqlcRK6DBk36km3q4ELqur+dng74FDglcCTgJ8Ajt9i\nBckJSdYmWbthw4YxQpIkSZIkjZPQrQf27gzvBdw+ouxq2u6WnXmvbLtr3gd8CHjC4ExVdVZVraqq\nVbvvvvt4kUuSJEnSVm6chO5y4IAk+yXZgSZpu3CwUJIDgRXAZQPzrkgykaU9Fbh2cF5JkiRJ0vRN\nmdC1Z9ZeAVwMXAecX1XXJDk9yTGdoscB51VVdea9n6a75SeSfIGm++Y753IDJEmSJGlrNeVjCwCq\n6iLgooFxpw0Mrxkx78eBx84wPkmSJEnSCGM9WFySJEmStPSY0EmSJElST5nQSZIkSVJPmdBJkiRJ\nUk+Z0EmSJElST5nQSZI0Q0mOSnJ9knVJThky/cwkV7V/NyS5qx3/uCSXJbkmyeeT/PrCRy9JWg7G\nemyBJEnaXJJtgbcBRwLrgcuTXFhV106UqaqTOuVPBB7fDt4DvLCqvpxkD+CKJBdX1V0LtwWSpOXA\nM3SSJM3MwcC6qrqxqu4FzgOOnaT8ccC5AFV1Q1V9uX19O/BNYPd5jleStAyZ0EmSNDN7Ard2hte3\n47aQZF9gP+CSIdMOBnYAvjIPMUqSljkTOkmSZiZDxtWIsquBC6rq/s0WkDwCOAd4cVX9cOhKkhOS\nrE2ydsOGDbMKWJK0/HgNHZAMa5NnXq5qVHu+PIxbD+OWXe71pelx/1KPrAf27gzvBdw+ouxq4OXd\nEUl2AT4CnFpVnx21kqo6CzgLYNWqVe7QkqTNmNDhD77psr40n9y/1COXAwck2Q+4jSZpe+5goSQH\nAiuAyzrjdgD+EXhPVX1gYcKVJC1HdrmUJGkGquo+4BXAxcB1wPlVdU2S05Mc0yl6HHBebX604jnA\nYcDxnccaPG7BgpckLRueoZMkaYaq6iLgooFxpw0Mrxky33uB985rcJKkrYJn6CRJkiSpp0zoJEmS\nJKmnTOgkSZIkqadM6CRJkiSpp0zoJEmSJKmnTOgkSZIkqad8bIEkLWFJFjuELaxYsWKxQ5AkSS0T\nOklaojZ/DvXsJJnT5UmSpKXBLpeSJEmS1FMmdJIkSZLUUyZ0kiRJktRTJnSSJEmS1FMmdJIkSZLU\nUyZ0kiRJktRTJnSSJEmS1FMmdJIkSZLUUz5YXJpnSea0rA+HliRJ0gQTOmmemYBJkiRpvtjlUpIk\nSZJ6yoROkiRJknrKhE6SJEmSesqETpIkSZJ6yoROkiRJknrKhE6SJEmSesqETpIkSZJ6yoROkiRJ\nknrKhE6SJEmSemqshC7JUUmuT7IuySlDpp+Z5Kr274Ykdw1M3yXJbUneOleBS5IkSdLWbrupCiTZ\nFngbcCSwHrg8yYVVde1Emao6qVP+RODxA4t5HfCpOYlYkiRJkgSMd4buYGBdVd1YVfcC5wHHTlL+\nOODciYEkTwR+DPjYbAKVJEmSJG1unIRuT+DWzvD6dtwWkuwL7Adc0g5vA/wF8AezC1OSJEmSNGic\nhC5DxtWIsquBC6rq/nb4ZcBFVXXriPLNCpITkqxNsnbDhg1jhCRJkiRJmvIaOpozcnt3hvcCbh9R\ndjXw8s7wzwKHJnkZ8FBghyR3V9VmN1apqrOAswBWrVo1KlmUJEmSJHWMk9BdDhyQZD/gNpqk7bmD\nhZIcCKwALpsYV1XP60w/Hlg1mMxJkiRJkmZmyi6XVXUf8ArgYuA64PyquibJ6UmO6RQ9DjivqjzD\nJkmSJEkLYJwzdFTVRcBFA+NOGxheM8UyzgbOnlZ0kiRJkqSRxnqwuCRJkiRp6RnrDJ0kaWlKht2I\neOZl7TUvSVK/mNBJUo+ZgEmStHWzy6UkSZIk9ZQJnSRJkiT1lAmdJEmSJPWUCZ0kSZIk9ZQJnSRJ\nkiT1lAmdJEmSJPWUCZ0kSTOU5Kgk1ydZl+SUIdPPTHJV+3dDkrs6016U5Mvt34sWNnJJ0nLhc+gk\nSZqBJNsCbwOOBNYDlye5sKqunShTVSd1yp8IPL59vSvwGmAVUMAV7bwbF3ATJEnLgGfoJEmamYOB\ndVV1Y1XdC5wHHDtJ+eOAc9vXTwc+XlV3tkncx4Gj5jVaSdKyZEInSdLM7Anc2hle347bQpJ9gf2A\nS6Y7ryRJk1lyXS6vuOKKO5J8dbHjGGI34I7FDqJnrLPpsb6mx/qanqVaX/sudgCzkCHjakTZ1cAF\nVXX/dOdNcgJwQjt4d5LrpxXl0rDg+1/euJBrmzXrZ3LWz+Ssn8n1tX7Gbh+XXEJXVbsvdgzDJFlb\nVasWO44+sc6mx/qaHutreqyvebEe2LszvBdw+4iyq4GXD8x7xMC8lw6bsarOAs6aaZBLgfvf5Kyf\nyVk/k7N+Jrc11I9dLiVJmpnLgQOS7JdkB5qk7cLBQkkOBFYAl3VGXwz8UpIVSVYAv9SOkyRpWpbc\nGTpJkvqgqu5L8gqaRGxb4N1VdU2S04G1VTWR3B0HnFdV1Zn3ziSvo0kKAU6vqjsXMn5J0vJgQje+\nXnd3WSTW2fRYX9NjfU2P9TUPquoi4KKBcacNDK8ZMe+7gXfPW3BLi/vf5KyfyVk/k7N+Jrfs6yed\nA4aSJEmSpB7xGjpJkiRJ6ikTOkmSJEnqqa0+oUty92zLJ/n9JNcm+XyST7QPkF2W5qi+Dkvyn0nu\nS/Jrcxfd4phuncxg+WuSvHJg3N5JPpnkuiTXJPnd+Yxhri1GnbXj353km0m+OJ/rX6qSHJ9kQ5Kr\nknwpyUmdaWuS3NaZ9o4k27TTzk5yUzvtqiT/a/G2QkuF7efkbC8btpGTsz1cGMu9/dvqE7phkmw7\nzVmuBFZV1WOBC4A3zX1US9cM6usW4HjgfXMfzVbjPuDkqjoIeDLw8iSPWuSY+uBs4KjFDmKRvb+q\nHgc8BfjjJN3nqJ3ZTnsU8NPA4Z1pf1BVj2v/3rKA8apHbD8nZ3u5YGwjp3Y2W197uGzbPxO6VpIj\n2qM57wO+MJ15q+qTVXVPO/hZmgfELmuzrK+bq+rzwA/nJ7rFkeQRST7dHsH5YpJDk/xOkjd1yhyf\n5K/b1y9sj0pfneSc6ayrqr5WVf/Zvv4ucB2w51xuz0JYyDoDqKpPA724NXySnZJ8pN3WLyZ5UZLz\nO9OPSPLP7euj2qP4Vyf5xDjLr6pvAeuARwyZvAOwI7BxDjZFy5zt5+RsLxu2kZOzPdzE9m/6fGzB\n5g4GHlNVN81iGS8BPjpH8Sx1c1Ffy8lzgYur6oz2KOxDgC/RPEz4VW2ZXwfOSPJo4I+Bp1TVHUl2\nnelKk6wEHg/8xyxiXyyLUmc9cRRwe1U9AyDJw4DXJdmpqr5HUy/vT7I78E7gsKq6adx6SbIPTaP1\n+c7ok5I8H9gX+GhVXdWZ9mdJTm1fv6CqpvXDVMue7efkbC9tI6die7iJ7d80eYZuc5+bzZdtuyOs\nAv5s7kJa0mZVX8vQ5cCLk6wBfrqqvltVG4Abkzw5yY8ABwKfAZ4KXFBVd0DzkOGZrDDJQ4F/AH6v\nqr4zFxuxwBa8znrkC8DTkrwxyaFV9W3gX4BnJtkOeAbwTzTdiT498Vkco15+Pck1wI3Am6vq+51p\nE11OfhTYKcnqzrRul5Ml15hp0dl+Ts720jZyKraHm9j+TZMJ3ea+N9MZkzyN5mjJMVX1g7kLaUmb\ncX0tR233hcOA24BzkrywnfR+4DnArwL/WM3DHwPM6iGQSbanaaj+vqo+OJtlLZaFrrM+qaobgCfS\nNGx/muQ0NtXLU4HL265E062X91fVo4FDgb9I8uND1v3fNI3nYbPbCm1FbD8nt9W3l7aRk7M93MT2\nb/pM6OZAkscDf0vTGH1zsePR4khzd7ZvVtU7gb8DntBO+iDwLOA4mi8kgE8Az2mPuDHd7hJJ0q7j\nuqr6yzkIf1EsZJ31TZI9gHuq6r3An9PUzaXt/5eyqV4uAw5Psl8731j1UlWXAecAW9z9rd2/fg74\nyuy2Qpqc7efWwzZycraHm9j+TZ8J3fQ9JMn6zt/v03QReSjwgfZi1gsXOcalZIv6SvKkJOuBZwN/\n257+Xg6OAK5KciXNkbQ3A1TVRuBaYN+q+lw77hrgDOBTSa4GpmpwTu3WI80dml4APDWbbqV79Lxs\n1fw6goWrM5KcS9MAHNiOf8l8bNQc+Wngc0muojl78fqquh/4MPDL7X/aLjknAB9s6+X9I5Y3zBtp\nuvjs3A6f1K7vizTXWL99TrZEath+Tm65t5dHYBs5mSOwPZxg+zdNac7cSpIkSZL6xjN0kiRJktRT\nPrZgiLZP8rBnWfxi++wKdVhfcyPJH9N0q+n6QFWdsRjx9IF1NlySF7PltQGfqaqXL0Y82nrYHkzO\n+pk5v+8nZ/00ttb2zy6XkiRJktRTdrmUJEmSpJ4yoZMkSZKknjKhkyRJkqSeMqGTJEmSpJ4yoZMk\nSZKknvr/Deq/9/BAC8gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1aa42390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 比较几种模型在训练集上的cv_results\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "ax = plt.subplot(121)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(['lr_L2', 'lr_L1', 'lsvc_L2', 'lsvc_L1', 'svc_RBF'])\n",
    "plt.title('Algorithm comparison of cv_results on the train set')\n",
    "\n",
    "# 比较几种模型在测试集上的准确率\n",
    "plt.subplot(122)\n",
    "plt.bar(np.arange(5), accuracy, tick_label=['lr_L2', 'lr_L1', 'lsvc_L2', 'lsvc_L1', 'svc_RBF'])\n",
    "plt.ylim([0.7, 0.8])\n",
    "plt.title('Algorithm comparison of predict accuracy on the test set')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每次的结果都不一样，从训练集的交叉验证的评价结果来看，LogisticRegression+L2 和 LinearSVC+L2的结果通常较好，而测试集上的准确率来看LogisticRegression+L1，LinearSVC+L2 和 LinearSVC+L1的准确率是一样的。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
